Apache Flink Use Cases and Architecture for Streaming Data Platform

Apache Flink - Streaming Processing Framework?

• Keep the data moving (Streaming architecture, How to treat stream event)

• Declarative access, e.g. Stream SQL, CQL

• Handle imperfections, e.g. late events, missing events, and unordered events.

• Integrate batch and streaming data.

• Data safety and availability. (Fault tolerance, Durable state)

• Automatic partitioning and scaling.

• Several APIs in Java/Scala/Python
• DataSet API - Batch processing
• DataStream API - Real-time streaming analytics
• Table API - Relational Queries
• DSL ( Domain-Specific Libraries)
• CEP - Complex Event Processing
• FlinkML - Machine Learning Library for Flink
• Gelly - Graph Library for Flink
• Shell for interactive data analysis

• True Streaming Capabilities - Execute everything as streams
• Native iterative execution - Allow some cyclic dataflows
• Handling of mutable state
• Custom memory manager - Operate on managed memory
• Cost-Based Optimizer - For both stream and batch processing

Streaming Data Visualization gives users Real-Time Data Analytics to see the trends and patterns in the data to take action rapidly. Click to explore about, Real Time Streaming Data Visualizations

How does Apache Flink support a Streaming Analytics System?

The requirements of a Streaming Analytics System are listed below:

Keep the data moving

• Bounded data
• Unbounded data
• Real-time streams
• Recorded streams

Declarative access

Apache Flink has Table API and SQL API, which are unified for both streaming and batch data. This implies that the same semantics can be used on all types of data. SQL and Table API are built upon Apache Calcite and leverage features such as parsing, validations, and query optimizations.

Handle imperfections

Using the process functions in Apache Flink, we can handle data imperfections and manipulate the event, time, and state of streaming data. Time-related features in Flink -

• Event time mode

• Watermark support

• Late data handling

• Process-Time mode

Integrate Batch and Streaming data

Data Safety and Availability

Fault tolerance Apache Flink is made to handle failures. Durable state Flink maintains a strong state via checkpoint from time to time. Checkpoints allow Flink to recover state and positions in streams to recover from a failed state. Flink interacts with the persistent storage to store checkpoints. We can configure the various back-ends such as Message Queues - Kafka, Google Pub/Sub, AWS Kinesis, RabbitMQ Filesystems - HDFS, GFS, S3, NFS, Ceph

Automatic partitioning and scaling

Apache Flink provides first-class support for authentication of Kerberos only while providing effortless requirement to all connectors related to security. Click to explore about, Apache Flink Security and its Deployment

Apache Flink Use Cases

Here are some concise use cases for Apache Flink:

1. Real-Time Analytics: Detect fraud, monitor systems, and trigger alerts.

2. Event-Driven Applications: Deliver personalized recommendations and content.

3. Data Pipelines and ETL: Process and transform data streams and integrate batch and stream data.

4. IoT Data Processing: Aggregate sensor data and create real-time dashboards.

5. Financial Services: Implement algorithmic trading and real-time risk management.

6. Telecommunications: Monitor network performance and manage billing systems.

7. Gaming: Analyze game data and update leaderboards in real time.

8. Log and Event Processing: Aggregate and analyze logs, and perform clickstream analysis.

9. Geospatial Data Processing: Provide location-based services and geo-fencing.

10. Machine Learning and AI: Serve real-time predictions and update models with streaming data.

What are the benefits of Apache Flink?

The benefits of Apache Flink are listed below:

Accurate latency Streaming engine

Custom Memory Manager

Flink contains its memory management stack, serialization, and type extraction components. It uses C++-style memory management, and User data is stored in serialized byte arrays in the JVM. Memory is allocated, de-allocated, and strictly implemented using an internal buffer pool.

Apache Flink Advantages

• Flink will not throw an OOM(out of memory )exception

• Reduction of Garbage Collection

• Very efficient disk spilling and network transfers

• No Need for runtime tuning

• More reliable and stable performance

• Built-in Cost-Based Optimizer

• Custom state maintenance

Native closed-loop iteration operators Flink support iterative computation. Flink iterates data by using streaming architecture. Its pipelined architecture allows the processing of the streaming data faster with lower latency. Flink uses an iterative algorithm tightly bound into the Flink query optimizer.

Unified Framework Flink is a unified framework that allows building a single data workflow that holds streaming, batch, SQL and Machine learning. Analyze real-time streaming data Process graphs , Machine Learning algorithms

An open source dynamic data management framework which is licensed by Apache software foundation and is written in Java programming language. Click to explore about, Apache Calcite Architecture and Streaming SQL

Why does Apache Flink Matter in Big Data Ecosystem?

Apache Flink in Production

In production, Apache Flink can be integrated with familiar cluster managers

• Haddop Yarn
• Apache Mesos
• Kubernetes
• Stand Alone

We can deploy Flink in the resource-manager-specific deployment mode, and Flink interacts with the resource manager in a specific, appropriate way. Flink communicates with the resource managers to ask for the resources the application requires from its parallelism configuration. In the case of a failover situation where a job fails, flink automatically requests a new resource accordingly. It has been reported that Flink can support -

• Multiple trillions of events per day.

• Multiple terabytes of state.

• Running on thousands of cores.

Manage the large distributed environments that form a complex cluster and are difficult to manage correctly. Click to explore How to Secure Apache Zookeeper with Kerberos.

What are Apache Flink's best practices?

Parsing command line arguments and passing them around in the Flink application. Getting configuration values into the ParameterTool Using the parameters in the Flink program.

Naming large TupleX types - Used POJO (Plain old Java object ) instead of TupleX for data types with many fields. Used POJOs to give large tuple types a name.

Instead of using -

 
Tuplell<String,String,String> var = new ...;

CustomType var=new . . . ;
public static class CustomType extends Tuplell<String, String, String>{

}
Using Logback instead of Log4j
Use Logback when running Flink out of the IDE/java application
Use Logback when running Flink on a cluster

What are the best tools for Apache Flink?

• Command Line Interface (CLI) operates Flink's utilities directly from a command prompt.

• Job Manager—This is a management interface used to track jobs, their status, failure, etc.

• Job Client—This is a client interface used to submit, execute, debug, and inspect jobs.

• Zeppelin - Zeppelin is an interactive web-based computational platform along with visualization tools and analytics.

• Interactive Scala Shell/REPL - It is used for interactive queries.

Conclusion for Apache Flink 

Apache Flink is a community-driven open-source framework for shared Big Data Analytics. Its engine exploits in-memory processing, data streaming, and iteration operators to improve performance. XenonStack offers Real-Time Data Analytics and Big Data Engineering Services for Enterprises and Startups.

• Read more about Real-Time Data Analytics Services

• Click to explore Big Data Managed Solutions for Enterprises

• Explore more about Solutions for Streaming Data Platform

Next Steps with Apache Flink Use Cases and Architecture

Explore how Apache Flink powers real-time data processing with its scalable, fault-tolerant architecture. Learn about key use cases, including event-driven applications, real-time analytics, fraud detection, and anomaly detection. Discover how industries leverage Flink to handle massive data streams efficiently, ensuring low-latency insights and high availability.

Contact Us Talk To Specialist

More Ways to Explore Us

Distributed Data Processing with Apache Flink

The Ultimate Guide to Apache Flink Security and Deployment

Stream Processing with Apache Flink and NATS