Using Flink’s Low-Level APIs To Create Streaming ETL Pipelines For Complex Data As Well As Complex Calculations

In this article, Vivekkumar Muthukrishnan, an experienced specialist in Data Engineering, delves into the intricacies of leveraging Flink’s low-level APIs to construct streaming ETL pipelines tailored for handling complex data and intricate calculations.

Using Flink's Low-Level APIs To Create Streaming ETL Pipelines For Complex Data As Well As Complex Calculations

With his wealth of experience, Vivekkumar talks about the challenges and strategies involved in this cutting-edge domain.

Flink is a powerful and extensible platform for processing streaming data and performing complex calculations. It is actively used in the industry to create ETL pipelines that allow real-time data extraction, transformation and loading.

However, handling large amounts of data or complex data structures can be challenging and time-consuming. Flink’s low-level APIs provide developers with a more flexible and controlled approach to data processing.

They allow full control over data flow and computation execution, which is especially important when dealing with complex data.

One common use case for low-level Flink APIs is working with irregular or hierarchical data. Irregular data may have a heterogeneous structure or contain nested fields, making it difficult to process using traditional methods.

With Flink’s low-level APIs, developers can write more flexible code that takes into account the specifics of the data structure and allows complex operations, such as filtering or aggregation, at different levels of the data hierarchy.

Another important aspect of the job is performing complex calculations. Computation models can be complex and computationally intensive.

Flink’s low-level APIs provide the ability to explicitly control the logic of computation execution, perform optimisations and use resources as efficiently as possible.

This is especially important when working with large amounts of data or complex algorithms, where even small improvements in performance can result in significant reductions in task execution time.

Flink’s low-level APIs also provide the ability to integrate with other tools and systems. This allows developers to utilise different data sources, such as databases or file systems, and transfer the results of data processing to different repositories or analytical tools.

With this flexibility, developers can create sophisticated ETL processes that are easily scalable and customisable. Their use for complex data and sophisticated computations allows developers to have complete control over data processing and computation execution.

This is especially important when dealing with irregular or hierarchical data, and when performing complex calculations.

Vivekkumar highlights basic principles for creating Flink’s low-level APIs, naming 5 of them.

The first is the definition of data sources. ETL pipeline starts with loading data from various sources – files, databases, data streams, etc.

In Flink, data sources such as DataStream and DataSource APIs are used for this purpose, which allow you to read data from various sources as streams.

The second principle is data transformation. After loading the data, you need to perform various operations on it, such as filtering, transformation, aggregation, etc.

Flink provides a powerful set of operators and functions to perform such transformations. Flink’s low-level API allows developers to create their own custom operators to implement specific data processing logic.

The third is the partitioning of data into key groups. In many cases, data needs to be split into key groups for more efficient processing.

Flink allows you to group data by key fields using the keyBy() operator. This allows you to perform aggregation operations and other calculations within each group independently of other groups.

The fourth principle is to handle data with side effects. In some cases, you need to perform operations that change application state or perform external actions, such as writing data to a database or sending a message to an external system.

Flink allows you to perform such operations with side effects by using the sink() statement, which can be used to write data to external systems.

The last principle is application state management. Some applications need to preserve state between operations to ensure data integrity. Flink provides state persistence using the statefulMap() operator, which allows you to store state between operations.

As for Process-Functions, Vivekkumar mentions that they are a collection of functional elements, each of which performs an elementary data processing operation in a threaded environment, providing access to key components of all threaded applications devoid of loops.

  • events (stream elements);
  • state (available only when applied to keyed streams, as Keyed State is used);
  • timers (event time and processing time are available only for keyed threads, as well as the state).

The processElement(…) calls processElement(…) for each incident while the function is running. The use of timers allows applications to respond to changes in both processing time and incident occurrence time.

Each call to processElement(…) receives a Context object that provides access to the incident timestamp and the TimerService.

The TimerService is a timer management service where you can register or delete a timer based on the incident time or incident processing time, and get the current processing time and watermark value.

When a timer is triggered, the onTimer(…) function is called. Each timer is always bound to a key that was created during processing, which allows controlling the state of this key when onTimer(…) is called.

When combining two threads, it is possible to use CoProcessFunction and KeyedCoProcessFunction methods, which include unique element processing functions – executeFunction1(…) and executeFunction2(…).

Illustrating the real-world applications of Flink’s low-level APIs, Vivekkumar paints a vivid picture of their utility across diverse domains.

From real-time analysis of complex data to machine learning on data streams, and from big data analysis using graphs to social media data processing and analysis, the applications span a broad spectrum of use cases, underscoring the transformative potential of these APIs.

In concluding the insightful discussion, Vivekkumar’s expertise offers invaluable insights into navigating the complex terrain of streaming ETL pipelines, equipping the audience with practical knowledge and strategic guidance for harnessing Flink’s low-level APIs effectively in today’s data-driven landscape.

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