Datazip Features

Reports interface for standard and self-service reports is intuitive and easy to use.

Requires a minimal number of steps/clicks to answer business question.

Offers a variety of attractive graph and chart formats.

Score cards visually track KPI's.

Provides business users an interface to easily design, refine and collaborate on their dashboards

Using formulas based on existing data elements, users can create and calculate new field values.

Business users have the ability to filter data in a report based on predefined or automodeled parameters.

Users can drill down and explore data to discover new insights.

Ability to search global data set to find and discover data.

Ability for users to share data and reports they have built within the BI tool and outside the tool through other collaboration platforms.

Tool automatically suggests data types, schemas and hierarchies.

Analyze current and historical trends to make predictions about future events.

Communicate complex information clearly and effectively through advanced graphical techniques.

Ability to handle large, complex, and/or siloed data sets.

Converts data formats of source data into the format required for the reporting system without mistakes.

Ability to (re)structure data in a manner that allows extracting insights fast and accurate.

Provides business users an interface to easily design and refine their dashboards and reports. (What You See Is What You Get)

Application Programming Interface - Specification for how the application communicates with other software. API's typically enable integration of data, logic, objects, etc with other software applications.

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

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

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.

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. 10 reviewers of Datazip have provided feedback on this feature.

As reported in 10 Datazip reviews. 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.

Ability to connect the analytics platform with a wide range of connector options for common data sources, including popular enterprise applications.

Connects to enterprise data governance software, or provides integrated data governance features to avoid misuse of data

Using formulas based on existing data elements, users can create and calculate new field values

Business users have the ability to filter data in a report based on predefined or automodeled parameters.

Allows the user to combine data from multiple sources into a functioning dataset.

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

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.

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

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.

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.

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

Enables non-technical users to build dashboards through intuitive, drag-and-drop interfaces

Enables automated report generation and scheduled delivery to stakeholders

Allows dashboards and analytics to be embedded into external apps with branding flexibility

Supports integration with major data sources like cloud data warehouses, SQL/NoSQL databases, and SaaS applications

Efficiently processes large datasets with minimal lag and ensures high performance under load

Maintains performance and uptime during high traffic from multiple users or teams

Supports semantic data layers, role-based access controls, and metadata governance

Integrates with Jupyter, Python, or R for custom analytics and modeling

Provides native tools for statistical analysis, forecasting, and trend prediction

Uses AI to generate textual summaries, key takeaways, and data stories from dashboards

Allows users to query data and build reports using conversational or plain language

Detects and notifies users about KPI anomalies or significant metric changes in real time

Recommends next questions, analyses, or exploration paths using autonomous AI agents

Learns from historical user interactions to improve and personalize query results over time

Tailors dashboard views and suggestions based on user roles, access levels, and past behavior

Supports AI-driven exploration via prompts or multi-turn conversations for iterative querying