About HGrid247

HGrid247-3.5

What is HGrid247-3.5 DE ?
HGrid247-3.5 DE is a multi-platform, drag-and-drop big data engineering ETL tool designed for both batch and stream processing.

HGrid247-3.5 DE enables users to design data engineering workflows easily using an intuitive graphical interface. It automatically generates optimized Java (and PySpark) code that can be executed on parallel and distributed processing frameworks. As a result, users do not need advanced programming skills to build scalable data processing pipelines.
Key Capabilities

Visual drag-and-drop workflow design

Automatic code generation

Support for batch and streaming workloads

Multiple distributed processing engines as build targets

Reduced dependency on manual coding

Supported Processing Engines and Build Outputs
HGrid247-3.5 DE supports ten processing engines as compilation and build targets:

Hadoop MapReduce

Output file:
<project-name>_mr.jar

Apache Spark 1.6 RDD

Output file:
<project-name>_spark1.jar

Apache Spark 2.x RDD

Output file:
<project-name>_spark2.jar

Apache Spark 2.x (Dataset)

Output file:
<project-name>_spark2ds.jar

Apache Beam 2.29.0 (Batch)

Output file:
<project-name>_beam.jar

Apache Beam 2.29.0 (Streaming)

Output file:
<project-name>_sbeam.jar

Apache Flink 1.12.x (Batch)

Output file:
<project-name>_flink.jar

Apache Flink 1.12.x (Streaming)

Output file:
<project-name>_sflink.jar

Apache Tez 0.9.1

Output file:
<project-name>_tez.jar

Apache PySpark

Output file:
<flow-name>.py

Summary
HGrid247-3.5 DE provides a unified, low-code platform for building scalable data engineering pipelines across multiple big data engines, making it suitable for developers, data engineers, and analysts who want powerful processing capabilities without complex programming requirements.