Data Integration Tools   

 METL / Metadata ETL
Designed to Manage 
Large Schemas

Currently in Beta
General Availability Q2 2021

Low Code ETL: A Data Dictionary
Manages ETL Functionality

Design Wizards Quickly Generate the
Data Dictionary from Data Sources

Scalability and High Availability based
on Kafka and  Zookeeper

Audit Reports Track PII and Other Critical Data for Compliance in Healthcare and Finance

Supports Most ETL Use Cases:
Streaming, Batch, Rest, ORM, JDBC and Files

Metadata Managed
Data Integration
Simplifies, Automates & Accelerates ETL

A Data Dictionary Manages ETL Functionality 

Design Wizards Generate the Data Dictionary From Any Data Source

Data Managed ETL Reduces Life Cycle Costs by 80%

Public Cloud, Private Cloud or On-Premise

Supports Most ETL Use Cases:
Streaming, Batch, Rest, ORM, JDBC and Files


METL's value proposition to ETL is similar to Low Code programming tools. Our design wizards guide users by extracting schema information to populate a data dictionary style repository. The data dictionary is extended with simple tags to implement all types of ETL functionality like data cleansing, validation, data transformations etc. METL's customizable intelligent rules engine implements the ETL from the metadata.


The declarative programming paradigm is an extremely popular methodology to simplify and automate all types of  technologies. It's main goal is to separate the business requirements of an application (what needs to be done) from it's technical implementation. METL has separated it's metadata into two categories. The data dictionary to support the business requirements and additional metadata to implement various data architectures.


Managing data is inherently more cost effective than managing custom programming code. We maintain the data dictionary in a metadata database. This metadata is a combination of the data model, schema and ETL requirements. Using our declarative ETL we are able to keep the data dictionary simple. We support ad hoc SQL for mass updates to the metadata. The metadata can also be managed as a master data management solution, something we call Master Schema Management. This allows application developers to synchronize with data integration. There are countless value propositions to data managed ETL.


Since we're data managed with metadata you can choose between a design time ETL designer wizards or a dynamically managed, rules based ETL server. These concepts work well together. Start with you initial design, then as new schema changes occur at the source they can be automatically added to the data dictionary. Schema changes can also be applied to the destination.


Our foundation is a Java platform with rules classes and a workflow to implement ETL. These are preconfigured for you. You choose one for your use case. Example include NoSQL to SQL, data warehousing, dimensional modeling, Salesforce etc. The metadata completes the ETL configuration. The platform supplies a multi-threaded ETL engine plus management of metadata, job/batch, schema, errors and cache. Our Java technology can leverage nearly any Java library to connect to almost any data connection.

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