What is Apache Flink?
The framework to do computations for any type of data streams is called Apache Flink. It is an open source as well as a distributed framework engine. It can be run in any environments and the computations can be done in any memory and in any scale. The processing is made usually at high speed and low latency. Also, the data is generated at high velocity. Dataflow diagrams are executed either in parallel or pipeline manner. The framework is written in Java and Scala. It processes only the data that is changed and hence it is faster than Spark.
Understanding Apache Flink
It is used for processing both bounded and unbounded data streams.
Bounded Data Stream: Stream that has specific start and end points are called finite streams.
Unbounded Data Stream: These are those streams that have no specific endpoint. Once started they do not terminate. To process unbounded streams the sequence of the stream should be maintained. Flink takes these streams as input, transforms the data, perform analytics on it and present one or more output stream as a result.
How does Apache Flink make working so easy?
The main objective of it is to reduce the complexity of real-time big data processing. It processes events at high speed and low latency. As flink is just a computing system, it supports multiple storage systems like HDFS, Amazon SE, Mongo DB, SQL, Kafka, Flume, etc. Flink also has high fault tolerance, so if any system fails to process will not be affected. It will continue on other systems in the cluster. Flink has in memory processing hence it has exceptional memory management.
The various subset of Apache Flink
In the architecture of flink, on the top layer, there are different APIs that are responsible for the diverse capabilities of flink.
- Dataset API: This API is used for the transformation of Datasets. It is used for operations like map, filter, group, join, etc. It deals with bounded Datasets. API runs batch execution for data processing.
- Data stream API: This API deals with bounded and unbounded data streams. Similar to dataset API it is used for transformation(filter, aggregation, windows functions, etc) of live data streams.
- Table API: This API enables the user to process relational data. It is a SQL like expression language used to write ad-hoc queries for analysis. Once the processing is done the resulting tables can be converted back into datasets or data streams.
- Gelly API: This API is used to perform operations on graphs. Operations like create, transform and a process can be done using Gelly API. It simplifies the development of graphs.
- Flink ML API: Along with big data processing learning from that data and predicting future events is also important. This API is a machine learning extension of flink.
What can you do with Apache Flink?
It is mainly used for real-time data stream processing either in the pipeline or parallelly. It is also used in the following types of requirements:
- Batch Processing
- Interactive Processing
- Real-Time Stream Processing
- Graph Processing
- Iterative Processing
- In Memory Processing
It can be seen that Apache Flink can be used in almost every scenario of big data.
Working with Apache Flink
It works in a Master-slave fashion. It has distributed processing that’s what gives Flink it’s lightning fast speed. It has a master node that manages jobs and slave nodes which executes the job.
It is the future of big data processing. Below are some of the advantages mentioned.
- Open source
- High performance and low latency
- Distributed Stream data processing
- Fault tolerance
- Iterative computation
- Program optimization
- Hybrid platform
- Graph analysis
- Machine learning
The core data processing engine in Apache Flink is written in Java and Scala. So anyone who has good knowledge of Java and Scala can work with Apache Flink. Also, programs can be written in Python and SQL. Along with programming language, one should also have analytical skills to utilize the data in a better way.
Why should we use it?
It has an extensive set of features. It can be used in any scenario be it real-time data processing or iterative processing. It can be deployed very easily in a different environment. It provides a more powerful framework to process streaming data. It has a more efficient and powerful algorithm to play with data. It’s the next generation of big data. It is way faster than any other big data processing engine.
Below are some of the areas where Apache Flink can be used:
- Fraud Detection
- Anomaly Detection
- Rule-based alerting
- Social network
- Quality Monitoring
- Ad-hoc analysis of live data
- Large scale graph analysis
- Continuous ETL
- Real-time search index building
Why do we need Apache Flink?
Till now we had Apache spark for big data processing. But it is an improved version of Apache Spark. At the core of Apache Flink sits distributed Stream data processor which increases the speed of real-time stream data processing by many folds. Graph analysis also becomes easy by Apache Flink. Also, it is open source. Hence it is the next-gen tool for big data.
Who is the right audience for learning Apache Flink?
Anyone who wants to process data with lighting fast speed and minimum latency, who wants to analyze real-time big data can learn Apache Flink. People having interest in analytics and having knowledge of Java, Scala, Python or SQL can learn Apache Flink.
How does this technology will help you in career growth?
Since Flink is the latest big data processing framework, it is the future of big data analytics. Hence learning Apache Flink might land you in hot jobs. You can get a job in Top Companies with payscale that is best in the market.
With all big data and analytics in trend, it is a new generation technology taking real-time data processing to a totally new level. It is similar to the spark but has some features enhanced.
This has been a guide to What is Apache Flink?. Here we discussed the working, career growth, skills, and advantages of Apache Flink along with the top companies that are using this technology. You can also go through our other suggested articles to learn more –