Hydrograph is the next-gen GUI-based data integration tool designed by Bitwise to enable greater efficiency from ground up during your ETL development. Hydrograph addresses the need for better and more agile ETL functionality on Hadoop in enterprises with big data workloads without having to write MapReduce/Spark code and is built on cloud native architecture for efficient data integration.
Built by practitioners who understand the pains of offloading ETL on Hadoop/Big Data, Hydrograph is engineered to accelerate ETL development in the big data and is available in both On-Premise and Cloud platforms (AWS, GCP and Azure).
Benefits of our Data Integration Tool
Hydrograph helps enterprises bridge gaps between the ETL tools that developers are familiar with, and Hadoop/Spark for meeting critical reporting and analytical requirements.
It provides the following key benefits:
- Easy-to-use: The Hydrograph development environment is convenient and easy-to-use as it requires minimal retraining for ETL developers.
- High Processing Power: Hydrograph enables you to harness the processing power of Big Data, resulting in up to 33% faster execution time in Spark compared to legacy ETL tools.
- Offloading: Hydrograph provides the ability to offload legacy Teradata, Netezza or Big Data Appliance ETLs to modern Big Data ecosystem.
- Scalability: Hydrograph empowers you to create scalable applications that can meet today’s technology requirements while seamlessly managing future growth.
- Use Case Applications: In an agile environment, Hydrograph gives you the ability to deliver new use case applications.
Features of Hydrograph
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
- Spark Batch, MapReduce
- Spark Streaming, AWS Glue
- 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
- 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
Over the years, Bitwise has partnered with businesses from diverse industries to successfully enable a strategic and efficient ETL development process with Hydrograph through a more modern data processing environment.
Discover Services and Solutions that can make your data do more
Enable the need for meaningful test data and reduce dependency on pulling data from higher environments.
Ensures a high quality of information in your enterprise, resulting in high customer satisfaction.
Automate conversion process, by allowing conversion from any data integration platform (ETL and PL/SQL) to any other platform.