Practical Benefits of Data Streaming
Streaming is not a new concept. It’s one that’s been part of the world since the beginning of time. Think of a stream of water, and you think of something that flows endlessly, without stopping until it reaches its final destination.
In the digital age, streaming is so ubiquitous that even technical novices understand it. A movie streams from a provider directly to its end destination - your TV, laptop, tablet, and so on - without being stored in between.
In the business world, streaming is being leveraged for data agility. It’s helping businesses address the need for real-time data in the age when that need has intensified. Like the river and the latest movie on your favorite streaming service, streaming has some practical, real-world applications that have been around for a long time.
Data streaming enables data agility - the ability to quickly move and combine data for better insights and operations. Understanding the value of data streaming means first grasping its contextual definition for the modern business and examining a real-world example. Streaming is key to establishing a digital value mindset. Sometimes, when you want to get maximum value out of your data, you need to go with the flow.
What is Streaming Data?
Again, streaming itself isn’t a complex concept. In the context of business data, though, establishing a common understanding is key to seeing the potential within your organization.
Streaming data is also known as event streaming processing. It’s the continuous flow of data generated by various sources - like how a river can have multiple feeders that flow into it and lead to the ocean.
Data stream processing technology allows data to be processed, stored, analyzed, and acted upon as it’s generated in real-time. Like a droplet of water that goes from a feeder to a river to the sea, data moves from the source and is acted upon, all without ever stopping.
Accomplishing this requires the right architecture and implementation, and there are several options. Streaming functionality can be implemented with a language like the Java API for streams or tools such as Kafka, Confluent, and Amazon Kinesis.
However, even the best tools won’t optimize your data streams if you have throughput and scalability challenges. Your architecture must be designed for scale to address the real-time nature and demand of data streaming.
A Streaming Data Scenario: Before and After
Although data streaming may be easy to conceptualize, having a real-world example to draw inspiration from is always helpful. We’ve helped several clients tackle their streaming data needs - here’s a client project example that represents the kinds of problems you can solve with a well-thought-out and architected data agility solution.
Initially, our client’s implementation for data streaming involved using multiple checkpoints in the form of AWS SQS queues to send log information from their system to Amazon S3 and then further on to DynamoDB for aggregation and storage.
In some ways, this implementation was excellent. One of the benefits of using an SQS queue is that all logs were guaranteed to persist to S3, and all data would eventually make it to DynamoDB. The logs were vital to the business, and losing data would mean a loss of visibility and traceability of customer data.
Unfortunately, the system also created some challenges. When a large volume of logs was generated from within the system that brokered the files from source to target, the queue would get backed up and take many minutes to process all of the messages in the queue. This created a delay of log messages to the target, resulting in a delay of crucial business information used by support, operations, developers, and even the customer.
Changes to this solution required first refactoring the initial implementation with an emphasis on real-time processes. The log data was sent to Amazon Kinesis to stream the data directly into Amazon S3.
The benefit of this implementation was it was infinitely scalable. It was able to scale up or down to meet the needs of the business and allow them to accomplish the real-time data processing needed based on the events generated within the system.
Of course, there is always a trade-off. In this case, the issues came in the form of the requirement for zero loss of data. Handling errors in the Kinesis streams is more complex than with the queues, as automatic retries with AWS SQS are no longer used. A custom error handling framework was created to ensure that the solution continued to prevent data loss. This ensured no data was lost along the way, and if any streaming data failed to make it into the S3 instance, the system could automatically retry sending it.
Like any implementation, there is give and take in designing a solution, and in this case, creating streaming data allowed the system to achieve immense scalability. However, other facets became more complex, like the guaranteed delivery of the data and its visibility. Understanding the benefits and limitations of the solution, and architecting appropriately to address all requirements, is key to successful data streaming implementations.
Other Uses for Streaming
We’ve focused a lot in this blog about the real-time applications of streaming data, but let’s be clear - streaming has many other uses. For example, it can be used for:
- Processing large files or large amounts of data without saving an entire file to memory
- Enhancing performance capabilities of a system
- Decreasing the memory utilization of servers that are running hot
By not loading data into memory, you gain advantages of speed, performance, and data management, in addition to the real-time capabilities.
The Benefits of Data Streaming
Streaming data brings with it the advantages of data agility. Data agility can power the development of innovative and differentiating applications and enables streamlined operations. Data streaming and data agility allow for:
- Addressing real-time needs of a business, like driving an improved omnichannel retail customer experience
- Creating opportunities for faster decision making, increasing the positive impact of your data, and decreasing the negative
- Decreasing server and application utilization and memory
Streaming is a necessity for countless organizations. It can be nuanced, challenging, and requires careful planning for seamless execution. However, it also provides immense benefits, especially for real-time applications. It’s the primary means of achieving real-time data, or at least as close as you can get to it.
When you think about data in motion, think about streaming data and the idea of the droplet of water making its’ way, nonstop, to the ocean.
If you need help creating your data agility and streaming data strategy and architecture, let’s talk. At Big Compass, we’ve helped companies understand and embrace the power and advantages that data-streaming can bring while avoiding the pitfalls. Contact us today to discuss how we can help you bring data streaming into your business.