AI Location Analytics

What is heavy.ai?

HEAVY.AI, established in 2013 by Todd Mostak and Thomas Graham, emerged as a software company specializing in analytics and location intelligence solutions. The company leverages the computational capabilities of GPUs and CPUs to facilitate the querying and visualization of extensive datasets, notably encompassing geospatial and time-series data. HEAVY.AI's platform is geared towards assisting both enterprises and public sectors in making critical, time-sensitive decisions through the analysis of substantial data sets. Noteworthy investors, including Google Ventures, In-Q-Tel, NEA, NVIDIA, Tiger Global Management, Vanedge Capital, and Verizon Ventures, have provided backing to HEAVY.AI, further reinforcing its market presence.

How much does heavy.ai cost?

Based on the information from online search results, the pricing of HEAVY.AI depends on the chosen edition and configuration. The available editions are Free and Enterprise. The Free edition supports up to 32GB of RAM, 1 GPU, and 3 active users. On the other hand, the Enterprise edition starts at 32GB of RAM, allows multiple GPUs, unlimited active users, and offers additional features like streaming, security, and distributed high availability.

The pricing for the Enterprise edition varies based on the selected AWS Marketplace fulfillment option and the EC2 instance type. For instance, if you opt for the hourly AMI option and select the g4dn.4xlarge instance type, the software cost is $1.92 per hour, and the EC2 cost is $1.204 per hour, resulting in a total cost of $3.124 per hour. The AWS Marketplace tool can help you estimate the combined software and infrastructure expenses based on your chosen configurations.

What are the benefits of heavy.ai?

HEAVY.AI offers a range of advantages, including:

• High-Performance Processing: The tool harnesses the substantial processing capabilities of contemporary GPUs and CPUs to seamlessly handle extensive datasets containing billions of records, ensuring smooth operations without noticeable delays.
• Comprehensive Data Exploration: Through HEAVY.AI, users can effectively combine and explore geospatial and time-series data. This integration provides a holistic understanding of the 'what,' 'when,' and 'where' aspects of the data, enabling a more complete perspective.
• Advanced Analytics and Visualizations: The platform integrates interactive visual analytics, hardware-accelerated SQL, and an advanced analytics and data science framework. This synergy empowers users to uncover concealed opportunities and potential risks within the data.
• Enhanced Data Analysis: HEAVY.AI offers the advantage of interactive machine learning and no-code joins, facilitating the augmentation of data analysis capabilities. This feature empowers users to refine their analyses and extract deeper insights from the data.
• Scalability Beyond Conventional Tools: The tool's scalability surpasses the confines of traditional business intelligence (BI) and geographic information system (GIS) tools. It is engineered to efficiently handle datasets of any size or complexity, addressing the challenges posed by massive and intricate data structures.

These benefits collectively position HEAVY.AI as a versatile solution for users seeking to process, analyze, and derive insights from substantial and intricate datasets.

How can I get started with heavy.ai?

To initiate your journey with HEAVY.AI, adhere to the following steps:

1. Select the Appropriate Edition and Configuration: Begin by determining the edition and configuration that aligns with your specific requirements. Evaluate the offerings and pricing of the Free and Enterprise editions through the official HEAVY.AI website. You can also leverage the AWS Marketplace tool to compute the projected expenses encompassing software and infrastructure based on the configurations you opt for.
2. Install and Configure HEAVY.AI on AWS: Proceed by installing and configuring your HEAVY.AI instance within the AWS environment. Consult the comprehensive guidelines furnished in the HEAVY.AI documentation. This resource will walk you through importing your data, establishing SSH access to your instance, and establishing your security key pair.
3. Familiarize Yourself with HEAVY.AI Tools and Features: Acquaint yourself with the array of tools and features provided by HEAVY.AI to effectively analyze and visualize your data. Employ the Immerse tool to craft interactive dashboards employing SQL queries and charts. For advanced analysis, utilize HeavyRF to execute machine learning techniques and statistical methods. Extend the functionality of HEAVY.AI by integrating a data science foundation and crafting custom charts and interfaces through tools like JupyterLab, Pyomnisci, Ibis, Altair, and others.
4. Explore Examples and Tutorials: Delve into the assortment of examples and tutorials available in the HEAVY.AI documentation. These resources elucidate how HEAVY.AI adeptly manages various data types and scenarios. Additionally, peruse the FAQs and Troubleshooting sections to glean solutions to common challenges.

Embarking on these systematic steps will facilitate your seamless initiation into the HEAVY.AI ecosystem, empowering you to efficiently analyze, visualize, and extract insights from your data.

Can I use heavy.ai on other cloud platforms?

Certainly, you have the option to utilize heavy.ai on cloud platforms beyond AWS. According to information provided on the heavy.ai website, the tool extends its support to operation on GPU or CPU servers, both in cloud environments and on-premises setups. Additionally, an alternative approach involves employing Docker, a versatile software platform designed for constructing, testing, and deploying applications through containers. Docker's compatibility spans multiple cloud platforms accommodating Linux, including Google Cloud Platform, Microsoft Azure, IBM Cloud, and others.

To leverage heavy.ai via Docker, you are advised to adhere to the guidelines outlined in the heavy.ai documentation. These instructions encompass steps such as obtaining and installing Docker, fetching the heavy.ai image, and launching the heavy.ai container. Moreover, it's imperative to fulfill the hardware prerequisites stipulated on the heavy.ai website to ensure optimal performance.

This inclusive approach enables users to employ heavy.ai in a variety of cloud environments, expanding its accessibility beyond AWS.

How does HEAVY.AI leverage GPU-acceleration for big data analytics?

HEAVY.AI leverages GPU-acceleration to enhance big data analytics by running SQL queries in parallel on both GPU and CPU architectures, which significantly speeds up the query process. This parallel processing capability allows users to query massive datasets, including geospatial and time series data, hundreds of times faster than other systems. The platform also supports GPU-accelerated spatial joins to speed up location analytics, providing instant analytics and insights on billions of records. This capability positions HEAVY.AI as a powerful tool for rapid data analysis and visualization.

What are the key features of HeavyIQ's conversational analytics?

HeavyIQ's conversational analytics is a feature that allows users to ask questions about their data using natural language. This innovative approach leverages a custom-trained Large Language Model (LLM) to translate natural language queries into SQL, generate relevant visualizations, and provide data summaries. HeavyIQ is designed to operate securely behind your firewall, allowing users to interactively explore vast datasets and immediately view actionable visualizations. This makes data interrogation more intuitive and accessible, especially for users who may not have extensive SQL expertise.

What advancements does HEAVY.AI's version 8.1 bring to data science?

HEAVY.AI's version 8.1 introduces several advancements that enhance data science capabilities. It includes Retrieval Augmented Generation (RAG) capabilities that help guide the LLM in performing conversational analytics. A new SQL operator, powered by LLM, assists with data clean-up, classification, sentiment analysis, and more tasks. Additionally, the version integrates built-in GPU-accelerated machine learning models, enabling users to incorporate predictive analytics directly from SQL queries. These features collectively enhance the analytical power and flexibility of the platform, providing users with more robust tools for data-driven decision-making.