Time series and event-based data streams are rapidly growing in importance. More than ever, these data streams are a vital component of applications – from core mission-critical applications to consumer games and social media apps that constantly measure behavior and engagement.

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IoT is one driver behind this trend, but we’re also seeing a growing number of different data sources too – and all of them are pushing data into systems.

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One thing you can count on with time series data is that it will keep growing exponentially. As time goes on, you accumulate more and more historical data. Added to this real-time data streams will also continue to expand as well – both horizontally and vertically, as more users and devices start to churn out data with growing complexity.

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Although the data will grow exponentially, the value you can get from it will not. This makes it incredibly important to architect your solutions with a perspective that encompasses this growing need. Your systems must be able to handle growing volumes of data streams and use intelligent solutions for economical long-term storage.

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We’ll look more at the storage side of this equation in a later blog, but for now let’s focus on how you can optimize the processing of data streams with analytics streams solutions.

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Managing your resources effectively with streamed time series data

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Something to consider is that your data ingress may come from a variety of different data stream sources. It might come from various IoT or other devices, third-party sources, and differing levels of reliability.

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As a result, you can expect these data streams to include data that is incomplete, unstructured, in differing schema, or containing errors. To optimize how this data is processed, you should use data processing to clean up your raw data, put it into a common schema, and ensure it is efficiently stored, indexed, and served when it’s needed.

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Alongside this, analytics streams are an essential part of how you leverage your raw data to generate value, by guiding decisions or triggering processes.
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Why analytics streams are so important for time series data

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Managing streamed data is all about maintaining the flow, but this doesn’t happen naturally. Your ingress of raw data will potentially be a mix of differently packaged information, so the analytics stream ensures that all of these are repackaged into the most efficient and effective format for your consumers.

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This ultimately ensures that your downstream data pipeline only contains the most relevant data for specific consumer needs.

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As data-driven decisions become more widespread, data streams must be reliable too. This makes it especially important to use anomaly detection tools so that errors or unusual patterns don’t make their way into the data pipeline, or to flag unusual activity as a signal for a decision.

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For certain use-cases, analytics streams with anomaly detection are especially important and can deliver vital business value of their own.

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For example, a financial institution needs to detect unusual patterns of behavior in real-time, instead of relying on historical batch processing. This way, they can take immediate measures to prevent fraud or identity theft. And what about medical devices, or IoT that measure real-time performance? These certainly must be able to act on patterns as they emerge.

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Analytics streams can also give you insights into how your data is being used and can give you a snapshot of real-time performance.

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Because time series data is all about the ‘here and now’, you can’t let this data get stale. You must squeeze the most value from it as soon as it arrives, when it’s still fresh. And scalable analytics stream solutions help you do this.

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Solutions and architecture for analytics streams

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The general architecture for streamed data consists of your data ingress, which is then processed into a ‘warm’ data stream, which is immediately available for fast queries.

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From here, the data flows in two directions: downstream to the data pipeline and the consumers, and in parallel to your analytics stream.
Your analytics stream also feeds data back into the pipeline as well, making it both a consumer and a producer. And, at the very end of the pipeline, we have an off-loader that sends your data stream to the most economical storage solution.

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There are two common solutions that can handle high volumes of data with minimal latency:

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Azure Stream analytics

This is a serverless analytics stream for processing high volumes of real time data. Easy to use with no-code editor so you can build an end-to-end streaming pipeline very rapidly. It’s fully integrated with Azure Event Hubs and Azure storage options, so if you’re using Azure and/or Apache Kafka this is a good solution. Includes built-in AI capabilities such as anomaly detection.

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Amazon Kinesis Data Analytics

This is another powerful solution for heavy workloads, and easy to use with Kinesis and AWS applications. You can rapidly create (using SQL) a complete set of stream processing applications for things like log analytics, clickstream analytics, IoT, and interactive data stream queries. Highly scalable and comes with built-in monitoring of analytics applications.

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Getting the maximum value from streamed time series data

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Both of the above solutions are very easy to use and are extremely scalable. This means you can rapidly start to add metadata to live data streams or transform it into the most valuable format.

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These also allow you to create the maximum value from your data by doing things like aggregating and summarizing data from a cluster of devices so the data stream is more streamlined for consumers, or creating a range of analytics applications that each produce their own distinct business value.

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The solution you choose will depend a lot on the specific analytics capabilities you want to use, the costs of each solution, and how well it works with your current stack. However, by selecting a highly scalable analytics stream, you can safeguard the future profitability of your system.

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