Ikahana
Ikahana is an open-source data analytics platform focused on enabling self-service data analytics. It allows users to easily ingest, visualize and analyze large volumes of data without the need for programming expertise.
opensource
selfservice
data-visualization
data-analysis
large-data
Ikahana: Self-Service Data Analytics Platforms
An open-source data analytics platform enabling self-service analytics, easy data ingestion, visualization, and analysis without programming expertise.
What is Ikahana?
Ikahana is an open-source data analytics platform designed to make big data analytics accessible to a wide range of users without programming expertise. It provides an intuitive graphical user interface that allows users to ingest data from various sources, visualize it using charts/graphs, and apply analytics and machine learning models.
Some key capabilities and benefits of Ikahana include:
- Flexible data ingestion from files, databases, APIs, etc.
- Pre-built connectors for common data sources like MySQL, PostgreSQL, CSV files
- Interactive dashboards for data visualization with drag-and-drop interface
- Support for ad-hoc SQL queries for data analysis
- Library of machine learning algorithms for predictive modeling
- Collaboration features to share analytics data and insights
- Scheduled jobs for automating workflows
- Open source allowing customization as per usage needs
With an emphasis on usability, Ikahana makes it easier for business analysts, data engineers and citizen data scientists to be more self-sufficient in leveraging data to drive decision making, without having to rely on data specialists. The dashboards and workflow features also facilitate better communication of data insights across teams.
Ikahana Features
Features
- Self-service data ingestion
- Drag-and-drop workflow designer
- Pre-built analytics components
- Visual data exploration
- Scalable big data processing
Pricing
- Open Source
Pros
Easy to use for non-technical users
Open source and free
Support for wide range of data sources
Visual data exploration capabilities
Can handle large data volumes
Cons
Limited advanced analytics capabilities
Less flexibility than coding analytics from scratch
Requires Hadoop infrastructure
Steep learning curve for advanced features
Official Links
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