Apache Beam
Apache Beam facilitates seamless data processing by reading from various sources, whether on-premises or cloud-based. It supports both batch and streaming use cases using a unified programming model. With extensibility for frameworks like TensorFlow Extended, it enables flexible pipeline execution across multiple environments, ensuring adaptability and community-driven support.
Top Apache Beam Alternatives
Timeplus
This powerful real-time data engine optimizes stream processing for diverse use cases, including DDoS detection and IoT analytics.
Apache Flink
Apache Flink serves as a powerful framework for distributed processing of stateful computations across both unbounded and bounded data streams.
Streamkap
Streamkap revolutionizes data streaming with a modern ETL platform that harnesses the power of Apache Kafka and Flink.
Google Cloud Datastream
Google Cloud Datastream is a serverless data streaming tool that enables real-time change data capture and replication from databases like MySQL, PostgreSQL, and SQL Server.
Decodable
Decodable revolutionizes real-time data streaming with a fully managed cloud service that harnesses the power of Apache Flink and Debezium.
Amazon Data Firehose
Amazon Data Firehose simplifies the process of capturing, transforming, and loading streaming data.
Arroyo
Designed for seamless integration in cloud environments, it scales efficiently from small applications to massive...
Amazon Managed Service for Apache Flink
It allows for real-time data transformation and analysis without the burden of managing infrastructure...
Estuary Flow
With options for public, private, and BYOC deployment, it offers unmatched flexibility and security...
Redpanda
Ideal for both Kafka users and newcomers, it provides quick integration, low-latency performance, extensive observability...
Insigna
With out-of-the-box connectivity and configurable data streams, it automates data preparation and enhances quality, allowing...
Baidu AI Cloud Stream Computing
Fully compatible with Spark SQL, it simplifies complex data operations through straightforward SQL commands...
Hitachi Streaming Data Platform
It features advanced capabilities such as data enrichment, automated processes, and seamless integration with multiple...
Yandex Data Streams
It supports Apache Kafka® and AWS Kinesis protocols, enabling seamless integration...
Apache Beam Review and Overview
The Apache Beam unified model is portable and is capable of running pipelines on multiple environments. This model provides you with the option of selecting the language you are comfortable with to start its processing.
Working
Apache Beam makes use of the open-source Beam to build a program, and this program defines the pipeline. The distributed processing backends of Apache Beam then executes this pipeline. The Beam comes into picture when parallel processing takes place. This software is capable of handling the processing of many smaller bundles of data.
It performs the ETL (extract, transform, and load) functions, which are the basis behind the movement of the data between different sources and media. The Beam SDK is capable of converting data regardless of its size. There is the option available for you where you can choose the Beam SDK. The pipeline runners translate the data that you define through the Beam pipeline.
Beam Capability Matrix
Apache beam enables you to build parallel processing pipelines by providing you with a portable API layer. This API layer works on the principle of the Dataflow model. The capability matrix displays the individual capabilities related to the pipeline and API layer. The matrix also shows the calculations associated with Apache Flink, Apache Hadoop, Apache Gearpump, etc.
The Direct Runner
This runner is responsible for executing the pipelines. It also keeps check on these pipelines and makes sure that they follow the Beam model. The main function of this runner is to perform the checks that make sure that the user never relies on the semantics, which is not created by the valid model. The Direct Runner enforces the immutability and encodability of elements. The Direct Runner is responsible for local level unit testing that, in turn, makes the system run faster and test easily.
Top Apache Beam Features
- Diverse data source support
- Unified batch and streaming model
- Multiple execution environments
- Extensible framework for projects
- Community-driven development
- Interactive Beam Playground
- Support for cloud and on-prem environments
- Flexible data sink options
- No vendor lock-in
- Simplified programming interface
- Real-time processing capabilities
- Comprehensive business logic execution
- Cross-functional team collaboration
- Integration with TensorFlow Extended
- Streamlined deployment processes
- Continuous updates and releases
- Robust error handling mechanisms
- Extensive documentation and resources
- Easy-to-use data transformations
- Versatile use case applicability
