By Starburst
How would you rate your experience with Starburst?
Real-Time Data Collection
Collects, stores, and organizes massive, unstructured data in real time
Data Distribution
Facilitates the disseminating of collected big data throughout parallel computing clusters
Data Lake
Creates a repository to collect and store raw data from sensors, devices, machines, files, etc.
Hadoop Integration
Aligns processing and distribution workflows on top of Apache Hadoop
Spark Integration
Machine Scaling
Facilitates solution to run on and scale to a large number of machines and systems
Data Preparation
Curates collected data for big data analytics solutions to analyze, manipulate, and model
Cloud Processing
Moves big data collection and processing to the cloud
Workload Processing
Processes batch, real-time, and streaming data workloads in singular, multi-tenant, or cloud systems
Real-Time Analytics
Facilitates analysis of high-volume, real-time data.
Data Querying
Allows user to query data through query languages like SQL.
Multi-Source Analysis
Integrates data from multiple external databases.
Facilitates the dissemination of collected big data throughout parallel computing clusters.
Data Visualization
Processes data and represents interpretations in a variety of graphic formats.
Data Workflow
Strings together specific functions and datasets to automate analytics iterations.
Governed Discovery
Isolates certain datasets and facilitates management of data access.
Embedded Analytics
Allows big data tool to run and record data within external applications.
Notebooks
Use notebooks for tasks such as creating dashboards with predefined, scheduled queries and visualizations
Data Integration
Consolidates, Cleanses and Normalizes data from multiple disparate sources.
Data Compression
Helps save storage capacity and improves query performance.
Data Quality
Eliminates data inconsistency and duplications ensuring data integrity.
Built-In Data Analytics
SQL based analytics functions like Time series, pattern matching, geospatial analytics etc.
In-Database Machine Learning
Provides built in capabilities like machine learning algorithms, data preparation functions, model evaluation and management etc.
Data Lake Analytics
Allows data querying across data formats like parquet, ORC, JSON etc and analyze complex data types on HDFS
AI/ ML Integration
Integrates with data science workflows, Machine Learning and artificial intelligence (AI) capabilities.
BI Tool Integration
Integrates with BI Tools to transform data into Actionable Insights.
Data lake Integration
Provides speed in data processing and capturing unstructured, semi-structured and streaming data.
On-Premise
Provides On-Premise deployment options.
Cloud
Provides Cloud deployment options (private or public cloud, hybrid cloud).
Scalability
Manages huge volumes of data, upscale or downscale as per demand.
Data Governance
Policies, procedures and standards to manage and access data.
Data Security
Restricts data access at a cell level, mask or hide parts of cells, and encrypt data at rest and in motion
AI Text Generation
Allows users to generate text based on a text prompt.
AI Text Summarization
Condenses long documents or text into a brief summary.
