Data Integration Tools   
Data Integration Tools   

METL / Metadata ETL

ETL for Large, Complex or Agile Data Systems

Powered by a Data Rules Engine and
Data Dictionary Style Metadata

Data Integration for Healthcare, Finance or Scientific Data

Extremely Low LOE to Implement Massive ETL Projects

Design Wizards Create ETL Dataflows
En Masse From Any Data Source

Dynamic Schema Integration with ORM, ReST and SQL/NoSQL. This Includes Salesforce and Other Cloud Vendors.

Scalability and High Availability
From Kafka and Zookeeper

Track PII and Other Critical Data and Obfuscation Rules

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

METL / Metadata ETL

ETL for Large, Complex or Agile Data Systems

Powered by a Data Rules Engine and Data Dictionary Style Metadata

Data Integration for Healthcare, Finance or Scientific Data

Extremely Low LOE to Implement Massive ETL projects

Design Wizards Create ETL Dataflows
En Masse From Any Data Source

Dynamic Schema Integration with ORM, ReST and SQL/NoSQL. This Includes Salesforce and Other Cloud Vendors.

Scalability and High Availability from Kafka and Zookeeper

Track PII and Other Critical Data and Obfuscation Rules

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

Synopsis

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

Declarative
Programming
Paradigm

The declarative programming paradigm is an 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 your business requirements and additional metadata implements your data architecture.

Data
Managed
ETL

Managing data is inherently more cost effective than managing custom programming code. A METL implementation is done across the entire data model all at once. METL functionality is tightly encapsulated and always invoked by metadata tags. This allows for a very methodical, rules based ETL implementation. Many architecture design changes can be made by simple metadata change, while other can be done via ad hoc sql change to the data dictionary. The metadata is user expandable that corresponds to custom rules classes. In addition we support User Defined Functions which can also invokable via metadata tags. Nearly all METL functionality is database vendor independent including data persistence and post processing.

Responsive
Dynamic
Application

You can choose between a design time ETL designer wizards or a dynamically at runtime. 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.

Java
Platform

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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