20 Data Streaming Interview Questions and Answers

Data Streaming is a process of continuously transmitting data between two or more devices. It is commonly used in applications such as video streaming, audio streaming, and gaming. When applying for a position that involves data streaming, you can expect to be asked questions about your experience and technical knowledge. In this article, we review some common data streaming interview questions and provide tips on how to answer them.

Data Streaming Interview Questions and Answers

Here are 20 commonly asked Data Streaming interview questions and answers to prepare you for your interview:

1. What is streaming data?

Streaming data is a type of data that is continuously generated by a source and delivered in real-time to a destination. This data can come from a variety of sources, such as sensors, social media, financial markets, and more. The key characteristic of streaming data is that it is generated continuously and in real-time, meaning that there is a constant flow of new data that needs to be processed as it comes in.

2. Can you explain the difference between batch processing, micro-batch processing, and continuous processing?

Batch processing is the processing of data in groups, or batches, as opposed to processing data one piece at a time. Micro-batch processing is a type of batch processing that processes data in very small batches, typically in the range of 1-10 seconds. Continuous processing is a type of data processing that happens in real-time, as data is being generated, as opposed to being processed in batches.

3. How do you differentiate between stream processing and message queuing or event processing?

Stream processing is a way of handling data as it is generated or received, in real time. This means that the data is processed as soon as it is available, rather than being stored and then processed later. Message queuing or event processing, on the other hand, can involve storing data and then processing it later, or processing data only when certain conditions are met.

4. What are some of the common challenges associated with big data streaming?

There are a few common challenges associated with big data streaming. First, it can be difficult to process and analyze data in real-time as it is coming in. Second, there is often a lot of data coming in very quickly, which can overwhelm systems that are not designed to handle it. Finally, it can be difficult to store all of the data that is coming in, especially if it needs to be kept for a long period of time.

5. Are there any advantages to using a cloud service for data streaming over an on-premises solution? If yes, what are they?

There are several advantages to using a cloud service for data streaming over an on-premises solution. First, cloud services are typically much more scalable than on-premises solutions. This means that they can more easily handle spikes in traffic or increases in data volume. Second, cloud services are usually more reliable than on-premises solutions, meaning that your data stream will be less likely to be interrupted. Finally, cloud services often provide more features and functionality than on-premises solutions, making them more powerful and flexible.

6. What are the most popular tools used for data streaming?

The most popular tools used for data streaming are Apache Kafka and Apache Storm.

7. What are the different layers in the Lambda Architecture?

The Lambda Architecture has three layers: the batch layer, the speed layer, and the serving layer. The batch layer is responsible for storing all of the data, the speed layer is responsible for processing new data as it comes in, and the serving layer is responsible for providing an interface for querying the data.

8. Can you give me an example of how real-time data streaming works in practice?

A good example of real-time data streaming can be found in the financial sector. Stock prices are constantly changing, and so financial institutions need to be able to receive and process this data as quickly as possible. To do this, they set up data streaming pipelines that take in the live stock prices and then perform various calculations on them. This way, they can make decisions based on the most up-to-date information available.

9. What’s the difference between structured and unstructured data in context with data streaming?

Structured data is data that can be easily organized into a specific format, such as a table. Unstructured data is data that does not have a specific format, and is often more difficult to work with. In the context of data streaming, structured data is generally easier to work with because it can be more easily organized and processed.

10. Is it possible to use Hadoop for data streaming?

Yes, it is possible to use Hadoop for data streaming. Hadoop is often used for batch processing, but it can also be used for data streaming. Hadoop can be used for data streaming by using the Hadoop Streaming API, which allows for the creation of MapReduce programs that can be used to process data streams.

11. We know that Kafka supports both batch and stream processing, so why does it still need Spark Streaming?

Spark Streaming is a tool that helps to process data in real-time, as it is being generated. This is different from batch processing, which processes data in batches, or chunks, that have already been generated. While Kafka can handle both batch and stream processing, Spark Streaming is still needed in order to provide the real-time processing that is often required for data streaming applications.

12. Does Apache Kafka achieve exactly-once guarantees for all its messages?

Apache Kafka does not provide exactly-once guarantees for all messages. However, it does provide exactly-once guarantees for messages that are not marked as “retryable.” This means that if a message is marked as “retryable,” it is possible that it may be delivered more than once.

13. What is your opinion about Apache Flume compared to other systems like Kafka?

Apache Flume is a great tool for data streaming, but it does have some drawbacks when compared to other systems. For one, it is not as scalable as Kafka. Additionally, it is not as easy to use and configure, which can make it more difficult to get started with.

14. Can you explain what a DAG (Directed Acyclic Graph) is in context with data streaming?

A DAG is a data structure that is used to represent a sequence of data processing steps, where each step is represented by a node in the graph. The edges in the graph represent the dependencies between the steps. A DAG is acyclic if there are no cycles in the graph, which means that the steps can be executed in any order.

15. What are Watermarks and Tumbling Windows?

Watermarks are a way of keeping track of progress in a data stream by assigning each piece of data a timestamp. Tumbling windows are a type of windowing function that allows for data to be processed in fixed-size chunks.

16. Can you give me some examples of when you would use a Watermark?

A watermark can be used in data streaming to identify the position of the stream up to which data has been processed. This is useful in cases where the stream needs to be processed multiple times or in case of a failure, so that the stream can be restarted from the last known good position.

17. In the context of data streaming, can you explain what checkpointing means?

In data streaming, checkpointing refers to the process of saving the state of a stream at a certain point in time. This allows the stream to be restarted from that point if necessary, which can be useful in the event of a failure or interruption.

18. What are the two main types of analytics performed on streaming data?

The two main types of analytics performed on streaming data are real-time analytics and historical analytics. Real-time analytics is used to analyze data as it is being generated, in order to make decisions in the moment. Historical analytics is used to analyze data after it has been collected, in order to gain insights about past trends.

19. What is a stream processor?

A stream processor is a type of computer software that is designed to handle data streams, in real time, as they are received. A stream processor can be used to perform a variety of tasks, such as filtering, aggregating, or transforming the data.

20. Why do we need stream processors if we already have distributed computing platforms like Spark or MapReduce?

Stream processors are designed to work with data that is constantly changing, or “streaming.” This is in contrast to platforms like Spark or MapReduce, which are designed to work with data that is static or only changes infrequently. Stream processors are able to handle data that is constantly changing because they are designed to process data in small batches, as it comes in, rather than all at once. This makes them well-suited for applications like real-time analytics, where you need to be able to quickly process large amounts of data that is constantly changing.