When building software or web applications, you can add analytics, but what does it mean to be real-time? Generally speaking, there are three types of analytics. The first one is dashboards and BI tools. These are normally used for internal purposes. The second one is user-facing analytics. These are analytics you provide to the end-users of your software or web applications. The third one is machine-learning, machine-powered, or machine-fed type of analytics. These are when you feed analytics or events directly into your systems and then have your systems do the processing automatically—like anomaly detection or fraud detection.
An important part of a real-time analytics system is its ability to ingest new data as soon as it is pulled in by a streaming source, to process all of this raw data into machine-readable data. Real-time analytic systems use data processing frameworks, including Apache Kafka and Apache Spark.
Before we learn about Kafka, let's learn how companies start. In the beginning, there is a source system and a target system, and there needs to be a data exchange between the two - That's pretty simple, right.
But what happens is that as the company grows, the source and target systems also increase, and there must be a data exchange between them, complicating the matter. For instance, if there are 4 sources and 6 target systems, there would be a need for 24 integrations.
Each integration comes with its share of difficulties:
Moreover, each time a source system is integrated with a target system, there will be an increased load from the connections. So, how do we solve this? Well, this is where Kafka comes in.
Kafka is an open-source, distributed streaming medium that allows for the development of real-time event-driven applications.
Kafka allows you to decouple data streams and systems.
The source systems will have their data in Kafka, and the target systems will source their data directly from Kafka, leaving out the hassle of manually integrating the data.
Kafka is super quick.
The reproduced records are replicated and partitioned to allow many users to use the application simultaneously without any detectable lag in performance.
Kafka maintains a high level of accuracy.
The data records ingested into Kafka are accurate a Kafka prevents data loss and maintains the sequence.
Kafka is also resilient and fault-tolerant
Because ingested data in Kafka is replicated, the margin for errors is greatly reduced.
These characteristics all together add up to a potent platform.
Some applications of Kafka in real-time data analytics and data processing include:
The goal of Spark is to provide a fast general-purpose cluster framework for large-scale data processing designed to overcome the limitations of MapReduce, which was the most common data processing method in Hadoop at the time of Spark development.
The foundation of Spark is based on the resilient distributed data set or RDD, a programming abstraction representing a collection of read-only objects split across a computing cluster.
Spark can create the RDD from text files.
Spark can create the RDD from text files, SQL databases, NoSQL databases, HDFS, cloud storage, and the list.
RDDs work for multiple functions.
RDDs allow for standard MapReduce functions but also join datasets filtering and aggregation.
The processing of RDDs is done entirely in memory.
The RDD is designed to hide complexity from users who then don't have to worry about where specific files are sent or what resources to store and retrieve.
Spark has fast processing.
Among many, one of the most significant attributes of Spark is its swift processing. Thanks to the RDD design and in-memory processing, its fast y processing makes it run significantly faster than other big data options.
Some applications of Spark in real-time data analytics and data processing include:
Both Kafka streams and Spark structured streaming are used in real-time analytics systems and for data processing, but both of these frameworks differ from each other in the following modes: