1. Accuracy: Data accurately represents the intended attribute values of a concept or event within the application context. DSP applications' operations and filters can be highly sensitive; testing such functionalities relies on the precision and relevance of values corresponding to the variable's concept.
2. Credibility: This concerns the data’s authenticity or whether it is believable as real-world data from the application's usage context. Addressing credibility in the DSP context is more complex due to additional factors, such as the temporal distribution of data, frequency of variable values, and intervals between messages. Furthermore, the 4Vs of Big Data (volume, velocity, variety, and veracity) introduce unique aspects to stream data.
3. Currentness: This relates to the data's age validity. Data characteristics can change over time in the DSP context, making them ineffective for testing (similar to how concept drift affects ML algorithms). Furthermore, application updates may cause data to be incompatible with newer application versions. Establishing policies for data lifecycle management can help address these issues.
4. Compliance: This involves data adhering to standards and conventions. DPS applications can consist of numerous entities interacting through various message patterns. At this point, test data must be compatible with the data structures in use. Adaptations may be necessary, and message schema management tools provide functionalities to provide compatibility between different message structures. In addition to structure, there are issues with standards and formatting not captured by schema management, like incompatible text encodings, GPS data coordinate patterns, variations between metric and imperial systems, and conflicts between signed and unsigned data.
5. Confidentiality: This concerns protecting sensitive information. DSP applications often operate in contexts involving confidential or sensitive data, such as personal, geo-location data, and financial data. Strategies to maintain the confidentiality of real data include anonymization, masking, using artificial intelligence techniques, and mirroring production data in shadow mode (details in Mirroring Production Data).

Historical data are assumed to be real data extracted from the application in production, which should also include metadata regarding temporal properties such as origin timestamps, network delay, and processing time. Historical data provides an initial model of data streams in the production environment.

1. Test Oracle Generation: Historical data may consist of inputs without expected outputs, and any existing outputs could be unreliable. Thus, generating and validating outputs for building a test oracle is necessary; this activity depends on documentation that includes data characterization and examples of correct outputs.
2. Coverage Limitations: Despite extensive historical data, it may not cover all potential future bugs due to application complexity and unmanifested bugs that could arise in production.
3. Outdatedness: Historical data might not exercise new functionality effectively and could become incompatible with updated message schemas. As conceptual data characteristics can change over time, their ability to simulate real-world conditions may decrease.

In this approach, replicas of the input data stream are redirected from the production environment to the testing environment, enabling the application to be tested with real data. This strategy allows the detection of critical failures that could disrupt the application's execution and facilitates the comparison of performance parameters and results accuracy across different application versions. Additionally, this strategy can be employed while ensuring privacy and data security through mechanisms such as shadow mode, which conducts automatic verification of parameters and execution results. Shadow mode, a finding from the GLR, is discussed in more detail within the article. Implementing this technique requires the availability of resources to replicate the infrastructure and skilled professionals to build the verification mechanism.

1. Property-based Data Generation: generates data by exploiting the message contract properties of the data stream. The technique can generate immense amounts of data, which can be refined by a process called shrinking, where the objective is to find the minimum data set to manifest the failure. this is a low-cost technique that requires effort to prepare and is, therefore, quick and easy to apply. The following tools support this technique: FlinkCheck, ScalaCheck, StreamData, and Ecto Stream Factory. (Check detais on property-based tests.)
2. Statistical Properties-Based Generation: results in more accurate data by configuring the generated data's statistical distribution and the temporal variations in the data distribution. This approach requires a solid understanding of mathematics and statistics. Custom scripts can be built using statistical libraries like Scipy in combination with fake data generation libraries. Pay-per-use services like Mockaroo offer ready-to-use solutions where users simply specify data generation properties.
3. AI-Based Generation: Natural language processing algorithms can extract information from project documentation, providing valuable input for machine learning algorithms in generating more meaningful data.

1. Mutation: This process generates new data by slightly altering the values of existing data.These minor modifications increase data diversity, reflecting the variability and complexity of real-world data while preserving the essential characteristics of the original data.
2. Machine learning: This method employs machine learning algorithms to extract features from an existing dataset and create models for generating new data. It can be used to expand limited test datasets, produce data variants to enhance test coverage and preserve real data privacy. Implementing this technique relies on skilled professionals in the field of machine learning.
3. Manual customizations: This process involves refining data to test functionalities that depend on a specific set of conditions, which synthetic data may not be able to trigger. Customizations can be achieved through iterative script execution processes and manual adjustment of generation variables until the desired result is achieved. This approach requires a professional who understands the application’s context and has access to comprehensive documentation detailing the functionalities.

Testing DSP applications requires understanding how time operates in production and testing environments. In production, the timing characteristics of data stream messages are inherent to the business context. However, these timings will differ in the test environment and can affect the validity of test results.

1. Clock Simulation: Controlling the system clock in the test environment is essential to emulate the time aspects of the production scenario as closely as possible. This feature is particularly useful for testing temporal windows, as the number of messages in each window can vary depending on the intervals between messages. The same applies to testing algorithms and functions that evaluate time factors. Be aware of how the event generation frequency in your test environment could influence test outcomes.
2. Speeding up the clock: Speeding up the clock in the test environment is a valuable strategy to minimize the duration of tests. Many stream processing platforms provide functions that allow for clock manipulation, including skipping certain test cycles, generating artificial watermarks, and configuring event timestamps to match an accelerated timeline. However, it's necessary to balance speed with result accuracy when employing this approach. Excessive acceleration of the clock may lead to losses in precision, which could obscure potential issues in the application. Essentially, if the test clock runs too fast, bugs tied to specific timing scenarios may go undetected.
3. Adjusting the Processing Time Interval: Calibrating the processing time interval is also crucial in testing DSP applications. Longer intervals can yield inaccurate results, while shorter intervals result in more frequent updates and more accurate results but at the expense of increased computational overhead.
4. Checkpointing Mechanisms: This is a valuable mechanism that periodically stores consistent snapshots of all states in timers and stateful operators, including connectors, windows, and any user-defined state. Platforms like Apache Flink come with built-in checkpointing features. This approach provides valuable state data to reproduce conditions in specific testing scenarios.
5. Testing Asynchronous Operations: Asynchronous operations are particularly tricky to test, as firing an event may involve timeouts and manipulating states stored in stateful operators. Testing these operations requires special attention due to their non-linear execution, and it's crucial to ensure that your testing environment can accurately monitor, manage, and validate these operations. One recommended tool for handling asynchronous operations is the Awaitility library, as it supports testing asynchronous operations by synchronizing these operations during the test, enabling the test to wait until certain pre-set conditions are met.

The non-determinism of DSP applications adds a layer of complexity to the testing process. The potential for different results to be produced in multiple runs complicates the result's consistency and the establishment of accurate test oracles. Non-determinism manifests at the system level when numerous variables contributing to non-determinism are present simultaneously. Several characteristics intrinsic to DSP applications, such as stateful operations, window-based operations, concurrent operations, and out-of-order messages, make applications inherently non-deterministic. DSP application functionalities often involve complex processes, encompassing multiple sequential and concurrent transformations. They may also rely on temporal windows and keep numerous shared states. Such functionalities tend to exhibit variation in their results when subjected to fluctuating network delays or changes in message order from data producers, complicating the construction of reliable test oracles.

1. Test Oracle Construction: Establish acceptable thresholds for result variations during test oracle construction. Then, statistical methods can be used to validate whether the observed variations align with the predetermined limits. The tool Great Expectations provides a feature for setting data variation thresholds in order to monitor data quality.
2. Deterministic Replay: This approach involves managing and identifying variables contributing to non-determinism, thus providing better control during test execution.
3. Chaos Engineering : In order to check results consistency, experiment repeated tests run application tests under non-deterministic variables like out-of-order messages and network and data volume oscillation.
4. Consider Typical Bugs Related to Non-Determinism: Watch out for typical non-determinism bugs, such as race conditions, ordering issues, state inconsistencies, lost, duplicate or delayed messages, and timeout-associated bugs.
5. DSP platforms provide features to deal with some aspects of non-determinism: First, event time processing and watermarks manage out-of-order and late-arriving data, allowing the treatment of issues arising from delays in messages caused by oscillations caused by non-deterministic factors. Second, state management and exactly-once-processing semantics maintain consistency in processing.

DSP applications run uninterruptedly 24/7 operations valuable to the companies' businesses. Therefore, this application must keep running in adverse conditions with disaster recovery capabilities to self-recuperate from crashes. In addition to application construction failures, such as a bug resulting from a programming error, we should also be concerned with failures arising from glitches and interruptions or oscillations of computational resources, networks and third-party services. For an application to be fault tolerant, it is necessary to build tolerance mechanisms. Such mechanisms involve first the autonomous ability to identify failures when they occur or predict failures about to emerge and then the action to prevent or reverse failures.

Chaos engineering plays a significant role in testing fault tolerance and system recoverability. It involves subjecting the DSP application to a controlled set of abnormal scenarios and verifying whether the system can restore checkpoints and resume regular functionality. Tools like the Thundra, Chaos Monkey and WireMock allow injecting errors, network oscillations, randomly terminating service, and simulating a range of possible failures to assess their impact. This process provides valuable insights for improving fault tolerance and recovery mechanisms by identifying potential weaknesses in the system. Below are some fault tolerance strategies that can be adopted in the context of DSP.

1. Infrastructure redundancy: This strategy involves having redundant backup servers ready to take over in the event of primary server failure. DSP platforms often provide easy-to-use integrated replica features. However, this strategy may be financially costly, and budget availability must be evaluated. Furthermore, the number of replicas increases system latency due to synchronisation overhead.
2. Scalability of hardware or network resources: Upon detecting an increase in demand, adjust resources to keep the service running within specified performance requirements. Elastic scalability is a feature of cloud infrastructures that performs this task. This mitigation action must consider the strategy for allocating financial resources. To scale up a broker cluster horizontally, consider the scaling capabilities of other services like APIs, consumers and producers to ensure that real-time processing is not affected.
3. Service redundancy: This strategy involves having alternatives for backup services that are automatically activated when a third-party service fails. For example, backup providers can easily replace SMS, encryption, and freight calculation services if they become unavailable.
4. Operation downsizing: In the face of a failure that cannot be automatically circumvented, the impacts of different mitigation strategies must be evaluated, such as temporarily interrupting the service, deactivating certain functionalities, or continuing to operate under extraordinary conditions. The mitigation strategy depends significantly on the application's context and will be tied to business decisions. For example, an e-commerce company can extraordinarily pre-authorize purchases from frequent customers when a particular payment service is temporarily offline. Conversely, a bank would prefer to turn down sensitive services when some security features are offline.
5. Version rollback: In the face of unstable behaviour or failures after the release of a new version, a mechanism for easy version update rollback is recommended to quickly contain problems in the production environment.
6. Operations rollback: when an operation has been delivering incorrect results due to a bug for some time. First, it is necessary to identify the period when incorrect results were delivered to reprocess them with a backup infrastructure. In addition, issues related to legal aspects and the business context must be evaluated, as reprocessing operations a posteriori can be useless or harmful. For example, credit card companies attend legal procedures for reversing and correcting incorrect charges.
7. Contracts compatibility: Large and complex DSP applications can have complex data schema with many message contracts. Updates can cause contract incompatibilities, especially if many modules interact and several teams promote changes in these modules and third-party services. Mitigation involves maintaining backward compatibility with contracts until contract updates propagate. Among the solutions in this context, we mention Avro, which supports compatibility for evolving contracts over time.
8. Fault Tolerance Tools: Chaos Monkey, developed by Netflix, is acknowledged for introducing random infrastructure failures. WireMock can simulate faults in HTTP-based services like APIs by mocking responses. Jepsen performs black box testing and fault injection on unmodified distributed data management systems. Thundra provides error injection capabilities, allowing for a more controlled testing environment where specific failure modes can be simulated and analysed.
9. Data Loss Strategy: In addition to the traditional mechanisms provided by DSP platforms to prevent data loss, another solution consists of synchronizing all incoming messages in a data lake. However, this approach might not be suitable for high-volume and intensive data scenarios.