Streaming SQL: Benefits, Challenges and 3 Significant Use Cases

For decades now, organizations have been facing an increasing need for real-time decision-making in order to stay ahead of the curve in the ever-changing competitive landscape. And one way to enable this is with the use of Streaming SQL. As opposed to traditional SQL, Streaming SQL can help organizations query and analyze data as it is being streamed. 

In this blog post, we’re going to walk you through what Streaming SQL is, its benefits, use cases, challenges and future trends. Read along to stay in the loop, and take a step towards adopting a holistic modern data stack. 

What is Streaming SQL?

Streaming SQL is a specialized form of SQL (Structured Query Language) that is engineered to process and query data in real-time streaming environments. Before the advent of the stream query engine, data needed to reach a destination before being queried. However, as the amount of data being generated each day increased, and a need to process them instantly to draw meaningful insights became imperative, the demand for a stream query engine was realized, that would process volumes of data with low latency. 

How is a Stream Query Engine Different from a Relational Database?

A stream query engine definitely shares a few characteristics with a relational database. But one must not confuse one for the other. 

There are several other differences between the two, as inferred from this excerpt from this research paper:

“Tables are the key primitive in a relational database. A table is populated with records, each of which has the same record type, defined by a number of named, strongly typed columns. Records have no inherent ordering. Queries, generally expressed in SQL, retrieve records from one or more tables, transforming them using a small set of powerful relational operators. 

Streams are the corresponding primitive in a streaming query engine. A stream has a record type, just like a table, but records flow through a stream rather than being stored. Records in a streaming system are inherently ordered, and in fact each record has a time stamp that indicates when it was created. The relational operations supported by a relational database have analogs in a streaming system and are sufficiently similar that SQL can be used to write streaming queries.”

What are the Differences between Streaming SQL and Traditional SQL?

There are several differences between SQL in stream and that of in a database, few of which will be discussed in this segment:

Criteria	Traditional SQL	Streaming SQL
Instant and Continuous Requests	Queries data in batch mode and returns results once all the data has been analyzed	Queries data in flight and continuously as the data is streamed, producing real-time results
Response Time	Responses are received after some time	First response is received after a while when the view is first materialized, post which responses are instantaneous
Access to Data	Processes and queries data irrespective of its order	Can only query data when streamed sequentially
Rate of Data Updation	Update rate is usually slower	Update rate is very quick
Windowing Operations	Does not have built-in windowing operations since it operates on fixed data sets	Introduces window functions to querying that define the scope and duration of data to be considered for calculations

What are the Benefits of Streaming SQL?

Streaming SQL is important for several reasons. Let’s glance through some of them in this section of our article.

• Real-time Insights: One of the most important benefits of SQL Streaming is its ability to provide real-time insights to organizations from streaming data. Industries like finance, cybersecurity, e-commerce, etc, can process the data as it arrives and reach important conclusions and make necessary decisions. 

• Continuous Data Processing: Streaming SQL allows organizations to process and query their continually increasing and streaming volumes of data. Organizations can see the changes in the results as the data streams by emitting changes and refreshing the results.

For instance, a traditional SQL query would look like–

SELECT * FROM pageviews;

A Streaming SQL query would add another clause at the end of the query–

SELECT * FROM pageviews EMIT CHANGES;

Adding EMIT CHANGES at the end of the syntax would continue to emit changes as new data arrives, rather than provide a single, definite result.

Streaming SQL provides you with the refreshed results anytime a change occurs, helping you make updated decisions.

• Simplified Development: Streaming SQL is very similar to the traditional SQL syntax. As a result, developers and data analysts can leverage their SQL skills to learn the new syntax promptly and easily build real-time streaming applications.

• Scalability and Performance: Apache Flink and Spark Streaming among others are Streaming SQL frameworks that provide scalable and fault-tolerant features. These frameworks distribute the load across multiple machines resulting in high-performance processing of large streaming datasets. 

3 Critical Use Cases of Streaming SQL

Streaming SQL has a myriad of benefits and naturally, it finds its uses in a number of scenarios. In this section, we’ll walk you through three of the more important use cases of Streaming SQL to understand how it could revolutionize the data space in a few years.

• Internet of Things (IoT): Streaming SQL is invaluable in IoT scenarios, as applications that are a part of this ecosystem generate large volumes of sensor data. This data can be queried quickly to detect anomalies, process sensor data and in the real-time maintenance of IoT infrastructure. For instance, Microsoft Azure IoT Hub has a built-in streaming SQL engine that can be used to analyze streaming data from connected devices.

• Fraud Detection and Security: Security threats and potential breaches, fraudulent financial transactions, network traffic and a compromise of sensitive data are a few things that Streaming SQL can help monitor. It allows organizations, like PayPal, to analyze streaming datasets continuously for patterns of anomalies and intervene before a sabotage occurs. 

• Health Monitoring: One critical use case of Streaming Query is in the healthcare industry, wherein it can be utilized for real-time health monitoring, sending alerts in case of emergencies, analyzing sensor data to detect anomalies, etc.

These are just a few examples of the real-life applications of Streaming SQL. Its real-time data processing capabilities make it an invaluable tool in numerous other domains.

Challenges and Opportunities of Streaming SQL

The ever-evolving data space faces several challenges, and Streaming SQL is no exception to it. However, these challenges are not without a silver lining. Let’s look at some of these challenges and opportunities in this section.

What are the Biggest Challenges of Working with Streaming SQL?

Streaming SQL is a powerful tool that has the potential to change the landscape of the data industry. However, data practitioners are often plagued by some of the challenges that it comes with. Let’s take a quick look at some of these challenges in this section.

• Data Consistency: Streaming data is often inconsistent, inaccurate and incomplete. This may lead to challenges in writing queries that return accurate results in a continuous manner, which is the underlying principle behind Streaming SQL.

• Latency: Streaming SQL must be executed quickly and continuously to keep up with the incoming stream of data. This might lead to additional strain on the processing system, especially in the case of large datasets, defeating the purpose of Streaming SQL.

• Data Velocity: Since streaming data usually arrives at a very high velocity, it can be difficult to process all that data in a timely manner.

• Data Types: Streaming data is usually heterogeneous, consisting of several data types and varieties. This can make it difficult for the stream query engine to process the data in an accurate manner.

How Can You Mitigate These Challenges?

Despite all these challenges, Streaming SQL can be incredibly useful to make fast data-driven decisions. There are several ways to address these challenges, some of which we are going to discuss in this section.

A variety of techniques, such as windowing, deduplication and watermarking can be used to tackle the challenge of inconsistent data quality. Windowing is an effective way of processing large datasets. In this technique, the data stream is divided into time intervals and data that falls within that time window is processed at a time. 

Deduplication is the process of removing duplicate data and watermarking ensures that there is a timestamp to each data point. This method helps in keeping track of the data points that have already been processed, and as a result, only data with newer timestamps are processed.

Streaming SQL can also use the techniques of batching, pre-aggregation and caching to address the challenge of latency. In batching, separate batches of multiple data points are grouped together to ensure faster and more accurate processing. 

Pre-aggregation can be applied to datasets that require aggregation. In this technique, datasets are aggregated before they are stored, resulting in faster querying. Finally, caching ensures that frequently accessed data is stored in memory, thus enhancing the performance of the queries that are executed on those datasets. 

With a wider adoption of Streaming SQL across various industries, one can expect more advanced technologies and techniques perfecting the stream query engine and resulting in a data processing approach that is truly real-time. 

Is Streaming SQL Closely Related to Streaming ETL?

Streaming ETL is the process of movement and analysis of data from the source to the destination in real-time, with almost zero latency. It enables organizations to make fast decisions and always provide fresh and updated data. 

Streaming SQL, as we have discussed already, uses a stream query engine to analyze data in real-time.

When Streaming ETL and Streaming SQL can be used together to achieve more accurate, powerful and faster results than either technology could achieve on its own.

For instance, you could use Streaming ETL to extract data from a streaming source, such as a Kafka topic, and then use Streaming SQL to process the data for fast insights. This could be used to detect banking frauds in real time, and analyze customer behavior.

It is important to understand that data pipelines can support Streaming SQL depending on the technology and tools used to build and execute the pipeline. Streaming SQL can be integrated into the data pipeline architecture to enable real-time processing and analysis of streaming data.

Automated streaming data pipelines, like Hevo Data, migrate and replicate data from your source to destination in near real-time, with significantly low latency. Integration with the right Streaming SQL frameworks and tools can help your business process data in no time and make swift decisions. 

Future Trends of Streaming SQL

Streaming SQL, although a new concept, has already started showing a fair amount of potential. In fact, in a couple of more years, most data-driven organizations around the globe will adopt this approach as their primary way of data processing. On that note, here are the future trends of Streaming SQL.

• Integration with Machine Learning: Machine learning can be used to analyze streaming data and identify patterns. Streaming SQL can be used to feed this data into machine learning models to train them and then use them to make predictions in real time. For example, a company could use Streaming SQL and machine learning to predict customer churn.

• Enhanced Event Processing: There may be advancements in event processing capabilities within Streaming SQL, including more sophisticated event pattern matching, complex event processing (CEP), and event-driven architectures. For instance, CEP is a technique for detecting patterns in streaming data. Streaming SQL can be used to implement CEP queries, which can be used to detect events that are of interest to businesses. For example, a company could use Streaming SQL and CEP to detect when a customer makes a purchase on its website and then send the customer a promotional email.

• Simplified Development and Tooling: Tools and frameworks, like Apache Flink and Spark Streaming, for building streaming SQL applications are likely to improve, providing more user-friendly interfaces, visual query builders, and code generation capabilities. This would simplify the development and deployment of streaming SQL applications and reduce the learning curve for developers.

• Integration with Cloud-Native Architectures: Streaming SQL frameworks are likely to integrate more seamlessly with cloud-native architectures, leveraging containerization and orchestration technologies like Kubernetes. This would enable easier deployment, scaling, and management of streaming SQL applications in cloud environments.

• Edge Computing and IoT Integration: Streaming SQL is expected to play a significant role in edge computing and Internet of Things (IoT) scenarios. It can be used for real-time analysis and processing of data at the edge, allowing organizations to extract insights and take actions closer to the data source.

Final Thoughts

In this article, we discussed what Streaming SQL is and how it is capable of causing a seismic shift in the data space. We spoke about the benefits and use cases of Streaming SQL, and also the challenges and possible solutions to the same. Finally, we realized that the future of this up and coming concept looks bright, and a few years down the line, Streaming SQL is going to be all the rage. 

Using automated streaming data pipelines, like Hevo Data, can be used for near real-time data processing. Hevo Data’s 150+ plug-and-play integrations (including 50+ free sources) is helping many customers take data-driven decisions through its no-code data pipeline solution. 

Want to take Hevo for a spin? SIGN UP for a 14-day free trial check out the pricing details to understand which plan fulfills all your business needs.

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