The growing demands for faster analytics and customer insights bring tremendous interest in streaming data technologies.
Streaming data has become a part of our everyday lives whether you realize it or not. Data can be gathered in real-time from online gaming, eCommerce and social media activity, GPS, and sensors. All of these new types of data have created an environment that necessitates deriving accurate insights before your competitors. In other words, having inaccurate analytics or even delayed insights can put you in a vulnerable position—a position where your competitors can overtake your market share by acting on industry and customer needs that you haven’t yet identified.
As a result, Kafka, Kinesis, and other real-time processing technologies have become an integral part of many companies’ technology stacks, allowing them to collect and analyze their data in real-time. In the past, streaming data was either limited by performance or was limited in the number of processes that can run—you can’t have both. However, Kafka and other streaming data frameworks now allow for a scalable, flexible way to move and process your streaming data.
Common Use Cases for Streaming Data Analytics
One major use case for streaming data is clickstream analytics.
Clickstream analytics allows companies to track their audience’s activity on their webpages. Companies will often track what pages visitors have been frequenting and the sequence of events that lead to viewers taking a major action—like making a purchase—on their site. Similarly, companies can track what links are clicked the most often and where website visitors tend to spend most of their time on any given page.
eCommerce companies find this especially important because it helps them minimize the chances that customers will abandon their shopping carts. Even more, it allows them to have real-time recommendations that can encourage buyers to add even more to their carts.
Traditional retail stores are also finding some use for streaming data. Many leading retailers are now using streaming data to have up-to-date data on their inventory and even their sales patterns at a particular store. This is allowing for companies to respond to their customers’ buying patterns at unprecedented speeds and granularity.
Another major use case for streaming data is real-time analytics for sensor data. The Internet of Things has been a buzzword for years now, and it heavily relies on data from sensors embedded anywhere from an airplane to a garbage can. Because Kafka and other technologies offer a scalable way to collect and process data, it is a natural choice for sensor data which can bring in terabytes of data every day. Sensor data allows companies to perform preventive maintenance on its machinery and to run several of its processes more efficiently. Streaming data is real-time analytics for sensor data.
Finally, many of the world’s leading companies like LinkedIn (the birthplace of Kafka), Netflix, Airbnb, and Twitter have already implemented streaming data processing technologies for a variety of use cases. These allow companies to have a more real-time view of their data than ever before.
Start Using Streaming Data with Talend Data Streams
Talend Data Streams is a free application that makes streaming data integration faster, easier, and more accessible. It makes complex streaming technologies simple and your data integration projects with Kafka and Kinesis easily done. It’s built for modern data formats including: AVRO, JSON, Parquet, and CSV, and supports Salesforce, AWS S3, Google Cloud Storage, and many cloud databases.
Plus, anybody with an AWS account can be up and running within minutes. Would you like to be able to work with streaming data? Be sure to check out Talend Data Streams to learn more about how you can get value out of these new types of real-time data.