Matia Features

Pull any required data from a variety of sources, including email, web pages, PDFs, and other documents.

Organize extracted data into a more easily digestible structure.

Amass extracted data in a variety of data formats like spreadsheets and .csv.

Stores data in cloud storage for access at any point.

Generate visual data representations from extracted data.

Monitors database functionality to verify baselines are maintained or exceeded.

Sends alerts via email, text, phone, and more when an incident or issue occcurs.

Captures logs for all database functions to garner greater information around issues or failures.

Monitors database query time for unusual execution times

Manually and/or automatically generates reports covering database performance

Follows database monitoring live information through graphical dashboards

Extract data from the designated source(s) like relational databases, JSON files, and XML files.

Cleanse and re-format extracted data to the needed target format.

Load reformatted data into target database, data warehouse, or other storage location.

Arrange ETL processes to occur automatically on needed time schedule (e.g., daily, weekly, monthly).

Capable of scaling processing power up or down based on ETL volume.

Ensures live debugging to alert if any failure during data sync.

Supports the creation of automated workflows and data pipelines.

Supports notifications in messaging applications such as Slack, and even in workflows.

Provides several pre-built connectors and API integrations to build data workflows.

Programmatic trigger of syncs via public APIs

Native support for incremental syncing

Move sync logs back into warehouse for auditing

Generate real-time depth analytics utilizing event metrics, logging and metadata.

Use custom or pre-built tests for buisness rules. to ensure data quality.

Involves automation capabilities to identify and track issues, failed operations by looking at historical trends.

Complete visibility of the data pipeline, and immediately notifies the data team if any issues. Ensures cross stack visbility.

View ETL process data via reports and visualizations like charts and graphs.

Record ETL historical data for auditing and potential data correction needs.

Track data and system backups, replication, and failover events.

Integrate with other management, resilience, or backup solutions

Be compatible with a variety of cloud and/or virtual infrastructure.

Monitor replication environment via GUI

Identify the different type of anomalies and receive alerts.

The data observability environment can be viewed from a single dashboard.

Provides immediate alerts for any anomalies or expected events.

Establishes lineage for the data pipeline - from data warehouse to the data user.

Support integrations with various business applications which support different data processes. Also, integrate with apps to provide alerts.

Provide as-before failover capabilities for cloud environments.

Services are offered under a pay-as-you-go or utilization-based purchase model.

Integrates data and data-related technologies into a single environment.

Provides metadata management capabilities.

Empowers the user via a self-service capability to manage data workflows.

Completely automates end-to-end data workflows across the data integration lifecycle.

Provides a high performance, flexibile analytics platform to support data management and embrace data driven decision making.

Collect and displays metrics across the data integration via a dashboard.

Involved solely in monitoring data pipelines, sending alerts and troubleshooting data.

Deploys testing capabilities such as report testing, big data testing, cloud data migration testing, ETL and data warehouse testing.

Supports analytical platforms and data pipelines across complex hybrid environments.

Supports migration of component or pipeline to different cloud environments.

Supports the creation of SQL models to query any data warehouse.

Detailed logging for auditing needs.

Real-time visibility into the reverse ETL process.

Configures data mapping from data warehouse to target tools.

View the reverse ETL data via reports and charts through data viz and sync progress.

Supports industry standards (GDPR, CCPA etc) before, during, and after the reverse ETL process.

Ensures data encryption across multiple levels of the reverse ETL process.

Pull any required data from a variety of sources, including email, web pages, PDFs, and other documents.

Allows users to generate text based on a text prompt.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives