Definition, Process and Challenges
Streaming Data is data continuously generated by different sources.
Such data should be processed incrementally using Stream Processing techniques without having access to all of the data.
In short, instead of handling/analyze/query a batch of data, we want to do it one data object after another.
Let's understand it even better with a simple draw -
First, streaming not allowing us to stop even for one millisecond. With that statement in our mind, how can process averages? sum? amount of appearances?
A new paradigm must take place, alongside random algorithms and prediction models.
We at Strech try not to make things complicated for you, so in simple words - We need to predict the data by counting enough appearances of it to make sure we can make a 90% accurate prediction of the data to come.
Second, In streaming, there is a great chance that your pipeline would break. Sudden changes in schema, faulty destination, too many moving parts can break your data streaming pipeline. In the best-case scenario, you lost some server logs, in the worst-case scenario your ML model lost crucial MRI images.
Why use Strech?
AWS Kinesis is an awesome tool, so are Apache Flink, Kafka Streams, and more. Strech born and built for data streaming and hence we make use of and developing the best-of-breed algorithms, blazingly fast streaming performance, the fastest onboard and migration experience, dedicated monitoring, data security, integrations, and much more.