Kinetica is an analytics platform and database designed to handle extremely large and complex datasets with ease. It is distributed, in-memory, and GPU-accelerated.
Combined into a single platform, Kinetica has all of the facilities needed to process:
- real-time streaming data
- geospatial data
- graph relationships
- and machine learning.
Kinetica can be accessed via:
- fast NoSQL-style key-value lookups
- an extensive API library
- and REST
The goal of this quickstart guide is to get you up and running quickly so that you can begin testing Kinetica with your own data. First, let’s take a quick look at the four main tools that help you manage and interact with the Kinetica Streaming Data Warehouse.
KAgent – Cluster Management and Maintenance
KAgent is the Kinetica installation, cluster management, and monitoring tool. From KAgent, you can:
- create new clusters
- scale your existing clusters
- upgrade a cluster
- review cluster details
- update the security configuration
- create a backup or set up a schedule
- manage Docker registries for Kubernetes and AAW
- start, stop, or restart any Kinetica service
- and monitor the health of your cluster.
GAdmin – Database Administration
GAdmin is the administrative interface of the Kinetica database. It is a single stop for the configuration, security, logging, and monitoring of your Kinetica database. GAdmin also provides the means for data ingestion, querying via SQL and REST APIs, executing user-defined functions (similar to stored procedures), and monitoring long-running jobs. Kinetica has access to five demo datasets containing a variety of data types and sharding strategies that are perfect for testing purposes; these datasets are available via the GAdmin Demo page.
Reveal – BI and Analytics Dashboard
Bundled with Kinetica is Reveal, a web-based BI visualization framework for querying and charting of data in an easy, interactive way. Reveal has a rich set of tools for making dashboards with charts, diagrams, and map visualizations. Multiple users can access these, and dashboards can be shared with others.
AAW – Advanced Analytics Workbench
AAW simplifies and accelerates data science and machine learning workflows in a scalable fashion. With AAW, users can create and manage batch or continuous ingests; create and manage TensorFlow featuresets and datasets; create, manage, and deploy RAPIDS and black box models and analytic functions; make inferences on models; and even audit models.
Next let’s import the data we will use for the remainder of this guide.