In today’s digital age, events are everywhere. Your business is a series of events. Every digital action – across online purchases to ride-sharing requests to bank deposits.

What is Fast Data?

A few years ago, we remembered the time when it was just impossible to analyze petabytes of data. The emergence of Hadoop made it possible to run analytical queries on our vast amount of historical data.
As we know, Big Data is a buzz from the last few years, but Modern Data Pipelines are always receiving data at a high ingestion rate. So this constant flow of data at high velocity is termed as Fast Data.

So Fast data is not about just volume of data like Data Warehouses in which data is measured in GigaBytes, TeraBytes or PetaBytes. Instead, we measure volume but concerning its incoming rate like MB per second, GB per hour, TB per day. So Volume and Velocity both are considered while talking about Fast Data.

Real Time Data Analytics and Streaming

Nowadays, there are a lot of Data Processing platforms available to process data from our ingestion platforms. Some support streaming of data and other supports real streaming of data which is also called Real-Time data.
Streaming means when we can process the data at the instant as it arrives and then processing and analyzing it at ingestion time. But in streaming, we can consider some amount of delay in streaming data from ingestion layer.

But Real-time data needs to have tight deadlines regarding time. So we usually believe that if our platform can capture any event within 1 ms, then we call it real-time data or real streaming.

But When we talk about taking business decisions, detecting frauds and analyzing real-time logs and predicting errors in real-time, all these scenarios comes to streaming. So Data received instantly as it arrives termed as Real-time data. This data gets generated by events occurs in a business.

Events are everywhere: Business is a series of events

In today’s digital age, events are everywhere. Every digital action – across online purchases to ride-sharing requests to bank deposits – creates a set of events around transaction amount, transaction time, user location, account balance, and much more. However, there’s a good chance you are thinking of events — and therefore your business — in the wrong way.

Real-Time Data Streaming Tools & Frameworks

So in the market, there are a lot of open sources technologies available like Apache Kafka in which we can ingest data at millions of messages per sec. Also Analyzing Constant Streams of data is also made possible by Apache Spark Streaming, Apache Flink, Apache Storm.

What is Apache Kafka?

Apache Kafka is a community distributed streaming platform capable of handling trillions of events a day. Initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log. Since being created and open sourced by LinkedIn in 2011, Kafka has quickly evolved from messaging queue to a full-fledged streaming platform.

Where Apache Kafka Fits In Enterprise

What is Confluent?

Confluent Platform
completes Apache Kafka.

Confluent created an open source event streaming platform and reimagined it as an enterprise solution. Streaming data as events enables completely new ways of solving problems at scale.