Intro to Streaming Databases

Most conventional SQL databases store data that doesn’t change very often. Examples are customer relationship management (CRM) applications or website applications that update every few minutes. Until recently, developers had to write extensive code to make databases work with continually changing or streaming data. Developing extensive microservices to process flows of data is costly and often fragile. SQL streaming databases handle data flows with minimal coding and produce real-time analysis for many different types of modern applications.

What is a Streaming Database?

A streaming database works with active data that changes continually. This changing data triggers actions in the database queries and applications instead of the other way around. Standard databases require human input to generate a response. For instance, a shopper buys a product in an eCommerce store. The human interaction triggers a response. The data in a streaming database is similar to a flowing river where it analyzes, collects, and processes these data flows and captures the data for use in real-time alerts, visualizations, queries, or applications.

Who is a Streaming Database For?

The rise of streaming database use can be attributed to the increased use of internet of things (IoT) applications, automated processes, and the use of real-time analytics for real-time decision making. Business is moving fast, and executives want to make decisions based on trending data rather than using past data stored in stagnant databases. Retailers like Amazon and eBay analyze customer transaction data streams to measure hot products and remove slow-moving products from online storefronts. Streaming databases can replace complex microservices, reducing the time and cost to develop applications. Product managers can increase their product’s time to market by analyzing results and test data in real-time. Use cases span industries and technical applications. For instance, in the pharmaceutical and biomedical sectors, scientists use these databases to monitor results from large-scale clinical studies improving drug viability and delivery times.

Why use a Streaming Database?

By using a streaming database, data users can ask questions and receive results in real-time even as the underlying data changes. There are numerous use cases for this innovative technology.

CyberSecurity Monitoring

Cybersecurity applications monitor constant flows of data through networks. Streaming database applications monitor the data for abnormalities and generate notices and alarms for IT security software to react and prevent harm to internal IT resources and data. Credit card companies and banks can issue real-time fraud alerts if a customer’s card or identity is stolen.

Real-Time Data Visualizations

Real-time business intelligence tools access streaming databases to generate graphs and analytical dashboards to provide ongoing reporting for critical business and technical uses. Monitoring streaming data can alert financial investors to market changes and fluctuations as they happen.

Live Alerts and Notifications

Streaming databases can provide live alerts with sub-second latencies. Receive notifications when data points exceed thresholds triggering alerts or additional downstream actions. Advertising executives can monitor streaming event data to understand when viewing statistics.

Simplifying Development of Real-Time Applications

Before the development of streaming databases, developers needed to find ways to join multiple databases and process changing data. The standard practice was to develop sets of microservices to handle the high-volume of complicated instructions required to process streaming data with traditional SQL databases. These databases remove the need to formulate microservices shortening the development time and reducing costs.

Streaming Database Best Practices

The benefits of streaming databases center around the speed of analysis and development time. Data is analyzed in real-time and reported with sub-second latencies powering decision-making tools and notifications. Databases should replace standard SQL databases when data flows increase, and traditional batch-mode data updates are inadequate for monitoring and visualizations. The streaming data can be processed using simple SQL sequences instead of complex microservices and can execute continuous queries to process real-time events. Using standard SQL to run a TPC-H decision-support benchmark to support multiple complexes joins and multiple-way joins for disparate data tables.

Conclusion

The increasing use of streaming data events is disrupting data warehousing and the use of standard SQL databases. The practice of using multiple microservices to join databases is being replaced by the use of streaming databases and simple SQL sequences. These new tools allow developers to process constantly-changing data and trigger events in real-time with almost instantaneous latency. Use cases range from financial alerts to cybersecurity monitoring and real-time visualizations. Industrial uses include monitoring IoT applications and machine learning algorithms. Product managers can shorten application development times by removing complex microservices with a few lines of SQL code.

Streaming databases are available as open-source code sets and supported by several vendors with complete documentation and support. The code is easy-to-use, and tutorials and sample codes are available to speed up this new technology’s adoption time.