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Get started with Hydrograph ETL for Big Data
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Hydrograph

ETL for Big Data

Hydrograph is a GUI-based ETL tool built to efficiently process big data workloads on premise or on the cloud without needing to write MapReduce/Spark code.

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ETL for Big Data
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Efficiently Process Big Data with GUI-based ETL Tool

Hydrograph enables enterprises to leverage their developers’ existing skillsets by providing an effective way to build ETLs on Hadoop using a drag-and-drop user interface harnessing the power of Spark and other big data processing engines. Hydrograph is available for both On-Premise and Cloud platforms (AWS, GCP and Azure).

Modern ETL tool to fit your current and future technology strategy

Key Benefits:
Easy to use

Easy-to-Use

Easy-to-use development environment that requires minimal retraining for ETL developers.

Faster Execution

Faster Execution

Harness the processing power of Big Data (up to 33% faster execution time in Spark compared to legacy ETL tools).

Offload Legacy

Offload Legacy

Ability to offload legacy Teradata, Netezza or Big Data Appliance ETLs to modern Big Data ecosystem.

Deliver New Use Case

Deliver New Use Case

Ability to deliver new use case applications in an agile environment.

Create Scalable Applications

Create Scalable Applications

Ability to create scalable applications that meet today’s technology requirements but flexible for future growth.

Modern ETL Tool key Benefits
Hydrograph-Key Benefits

Explore Hydrograph Enterprise Version

Hydrograph Enterprise Version

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Features

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Input / Output Components

  • File I/O: Delimited, Fixed Width, Mixed Scheme, XML, Avro, Parquet
  • Hive I/O: Text, Parquet, RC, ORC, Avro, Sequence
  • RDBMS: Oracle, Teradata, MySQL, Redshift, MS SQL Server, Generic JDBC I/O, IBM DB2 I/O, Netezza I/O
  • Semi-Structured Files: EBCDIC, JSON, Regex Input, Excel files
  • Kafka I/O
  • Cloud Data Warehouse: Snowflake I/O, BigQuery, AWS Glue, Cloud SQL, PubSub
  • NoSQL: MongoDB I/O, Hbase I/O, Cassandra I/O

Execution Engine

Execution

  • Spark Batch, MapReduce
  • Spark Streaming, AWS Glue
Usability

Usability

  • Subjobs and parameterization for creating reusable jobs
  • Execution Tracking visuals
  • Generic Jobs – By externalization of transform logic
  • Problems View
  • Grid view for displaying logs
  • Unit Testing Framework
  • Schema Import Wizard
Transformation Capabilities
  • Date, String, Numeric Functions for transformations
  • Standard ETL transformation components
  • Support for encryption/decryption, hashing, geospatial functions
  • REST and SOAP web service component
  • Conditional schema while reading files
  • Vector Functions
  • CDC (Change Data Capture) component

Ready for a modern big data ETL tool?

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Helpful Resources

Case Study

ETL Offload in a Hadoop Environment using Hydrograph
Read More

In The News

Bitwise announces support for Microsoft Azure cloud databases with Hydrograph ETL for Big Data development tool
Read More

Blog

Traditional ETL vs ELT on Hadoop
Read More
solution-summary
Solution Summary

Migrating Legacy Ab Initio ETL to Hydrograph on AWS Cloud

Read More

On-Demand Webinar

Hydrograph ETL Development Tool for Big Data and Cloud Platforms

Hydrograph is an enterprise-class, GUI-based ETL development tool designed...
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Bitwise provides comprehensive solutions for all your data projects

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Make your move to the cloud with confidence and take full advantage of ever-evolving tools to accelerate innovation and lead by disruption
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Risk-free conversion and optimization of source ETL jobs to a target ETL tool with maximum automation.
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Modernize and transform Enterprise Data Warehouse reporting systems to become more competitive for meeting today’s business challenges
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