Nexla Features

Functions when integrated with multiple forms of external applications.

Allows admisistrators to manage and organize storage, networks, and datacenters.

Monitors performance and statistics related to memory, caches and connections.

Alerts users when unauthorized parties or malware is detected allowing administrators to remedy the issue.

Secures data at rest and in transit from unauthorized access.

Allows administrators to organize components of the database to maintain instance efficiency.

Allows the creating and relinquishing of database access credentials.

Documents access and alterations of the database for analytics and reporting.

Expands functionality while maintaining balanced loads. Serves growing demand without reducing functionality.

The abillity to restore files to its original loacation or alternative sources on-demand.

Stores historical records of information for reverting or reference.

Helps users navigate databases by labeling and organizing datasets.

The ability to process large amounts of data.

The ability to process data from a wide variety of sources and formats.

The ability to work seamlessly with another software platform.

Processing data from a variety of sources in real time as it arrives.

Tools to visualize and analyze data.

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.

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

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

Offer backup and/or recovery options for data being migrated to the cloud.

Integrates with a variety of sources and/or migration targets.

Maintain user access and security features associated with pre-migrated data in the post-migration environment.

Real-time view and status of every component in the cloud environment.

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.

Migrate data from on-premises or cloud solutions to a separate cloud environment or solution.

Be compatible with a variety of data types for migration.

Send alerts for migration failures, and log migration processes to show possible root causes for failures.

Copy and store the same data in multiple locations.

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

Transform or encode data before being moved to the cloud.

Data masking provides an additional layer of access control that can be applied to tables and views.

Verifies identity of database users.

Allows users to gain statistically valid insights from their data while protecting the privacy of individuals.

Employs data encryption both at rest and in transit.

Comply to key industry standards like SOC 2, ISO 27001, PCI DSS, HIPAA etc to protect and safeguard data.

Supports secure communication protocols like FTPS, SFTP etc

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.

Helps in improving the availability of data.

Uses machine learning algorithms to gain actionable insights.

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.

Supports event or transaction based integrations that react to changes in real-time

Provides a web-based interface for designing, documenting, and testing APIs.

Allows for integration development through the visual development of integration logic flows with the help od a drag and drop user interface.

Facilitiates API development and integrations with prebuilt connectors, templates, and examples.

Provides the ability to create connectors from existing services and APIs in catalog.

Provides prebuilt, reusable connectors and workflows for all user integration requirements.

Enables multiple tenants (customers) to securely share physical computing resources.

Console for monitoring resource utilization, system health, ability to start and stop processes, etc.

Enables data routing on configuration basis and management of complex workflows through an orchestration engine.

Facilitates to and fro data map according to the data model between applications/web services.

Provides standard tools and functions to convert data values from the data format of a source system into the data format of a destination system.

Supports and oversees the management of API products across their full lifecycle

Integrates data from disparate sources, without physical data movement

Provides one or more methods to securely transfer data from one location to another through a network

Provides integration to BigData sources such as Hadoop and other NoSQL sources (MongoDB, Cassandra and HBase)

Provides Integrations to EDI service providers.

Supports deployment of integration apps both on-premisis and cloud environments/

Supports deployment of integration apps to a managed cloud environment

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.

Indexes metadata descriptions for enhanced insights.

Ensures automation of metadata across the organization. Dynamically improves data management processes.

Embedded collaboration using APIs and in-app integrations.

Provides insights into original data sources and transformations made to data sets from source to consumption of data.

Provides an interface to collect and evaluate data to identfy patterns and outliers.

Constantly processes metadata to provide intelligent insights.

Provides actionable insights, and generates notifications and alerts to help teams make informed decisions.

Supports 360 degree data visibility via a single dashboard.

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.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

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

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

Improves performance based on feedback and experience

Makes informed choices based on available data and objectives