Matt Brown, Product Manager, Kinetica & Amit Kesarwani, Principal Solutions Engineer, Kinetica

The supply chain and logistics ecosystem faces more complexities than ever before with drastic fluctuations in supply and demand. With modern tools, you can harness the wealth of data available today to respond with in the moment, data-driven decisions. In this talk we will show how Kinetica seamlessly brings together relational, graph and ML analytics on streaming data to solve supply chain and logistic use cases.

DURING THIS TALK WE WILL COVER:

• How real-time analytics can be applied predictively, or post-mortem
• Deploying a predictive model in Kinetica for demand forecasting
• Leveraging streaming data to gain real-time visibility into your supply chain
• Using graph solvers to execute route optimization and exception management
• Applying powerful visualization and geospatial analytics for fleet management

The post COVID-19 and Supply Chain Agility: How a Streaming Data Warehouse Keeps You Always On-Time and Fully Stocked appeared first on Kinetica - The Real-Time Database.

When a severe snowstorm hits you don’t want to bring a shovel when you really need a  plow. As enterprises confront a streaming data snowstorm in the cloud, they need to be thinking about new architectures and advanced analytical techniques to stay at the forefront of their industry. Implementing a cloud data warehouse strategy that suffers from the same issues as a traditional data warehouse is not the answer. As companies like Snowflake have proven, there is strong demand for cloud data warehouses to help consolidate the legacy data warehouse ecosystem. These systems make it extraordinarily easy to onboard data into your system, but over time can become highly complex to query and suffer from the same batch data constraints of the traditional data warehouses world. In this webinar, we will provide an alternative to the traditional cloud data warehouse conundrum, as well as guidance for a cost effective solution with Kinetica’s Streaming Data Warehouse.

IN THIS TALK WE WILL COVER:

• A streaming data warehouse for high performance analytics as well as  integrated OLAP, streaming, location, graph and machine learning analytics in one platform
• The solution for the use cases where Snowflake investments get hit by a snowstorm of streaming data – A product that is purpose-built, distributed, high performance data ingestion, which then makes data available for query as soon as it lands
• How we helped a customer who needed to do rapid analysis on highly complex data sets at high concurrency and lower their overall cost of data warehouse ownership
• Provide a path to lower total cost of ownership, higher performance & enhanced capabilities

DIPTI JOSHI Director, Product Management Kinetica

NIMA NEGAHBAN CTO & Co-Founder Kinetica

The post When Snowflakes Become a Snowstorm: Replicating a Legacy Data Warehouse in The Cloud is Not The Answer appeared first on Kinetica - The Real-Time Database.

Intelligent mapping apps can route us based on traffic, tolls, and other basic factors, but don’t we all wish they could automatically take into account the most beautiful route, the safest, or the one with the best restaurants along the way? In this Tech Talk, we’ll discuss leveraging Kinetica’s converged analytics to ingest, process, visualize, and inference millions of images to determine the most scenic route possible.

IN THIS TECH TALK WE WILL COVER:

• Deploying machine learning models and semantic segmentation at scale with Kinetica
• Mapping millions of images and inference to determine the best route
• Live demo of Kinetica’s converged analytics, which avoids integration of multiple complex systems with NxM interfaces
• Why Kinetica for this build

The post What Intelligent Mapping Could Be: Machine Learning With The Kinetica Streaming Data Warehouse appeared first on Kinetica - The Real-Time Database.

Utilities are constantly dealing with threats and hazards, including hurricanes and wildfires, that disrupt service, threaten citizen safety, and incur significant damages. For emergency managers tasked with preparing for disasters as well as coordinating swift disaster response, leveraging data and analytics to enhance operational workflows is critical. In this Tech Talk, we’ll share how Disaster Tech has developed an innovative platform to help emergency managers and utilities analyze and visualize data at massive scale and high performance to optimize disaster response and accelerate time to restoring services.

IN THIS TECH TALK WE’LL DISCUSS:

• How Disaster Tech, Kinetica, and NVIDIA, partner to offer a “single pane of glass for data and analytics” for emergency managers at utilities
• How the Disaster Tech platform helps utilities to better serve their communities and while also saving time, money, and lives
• Example applications of the Disaster Tech platform for utilities where new capabilities are unlocked through the use of high performance analytics
• How AI can be applied for storm prediction and restoration
• A demo of the Disaster Tech platform for utilities in scenarios like hurricane emergency response

The post Accelerating Data-Powered Disaster Response for Utilities with Disaster Technologies and Kinetica appeared first on Kinetica - The Real-Time Database.

In this Tech Talk we’ll discuss combining the optimization benefits of SQL with the flexibility of a procedural language. By using User Defined Functions (UDFs) with the Kinetica Streaming Data Warehouse, developers, analysts, and others can automate processes like ETL, common analytical tasks, and data re-labeling to improve performance, especially when working with massive datasets. We’ll discuss what’s unique about Kinetica’s UDF functionality and how it fits into the context of other popular UDF implementations, in addition to providing a demo of Kinetica’s capabilities in this arena.

IN THIS TECH TALK WE’LL COVER:

• The value of User Defined Functions
• Spark/Hive vs. SQL vs. Kinetica – benefits of each for UDF
• An overview of UDFs in Kinetica
• Distributed vs. non-distributed UDFs
• Real-world use cases for UDFs
• Demo

The post Automate the Boring Stuff with User Defined Functions: Enhancing Performance for Analytics at Scale appeared first on Kinetica - The Real-Time Database.

The Kinetica Streaming Data Warehouse gives organizations up-to-the-second results that incorporate all their data in a powerful, unified data warehouse. In this Tech Talk, we’ll discuss Kinetica’s unique ability to combine graph analytics with historical, streaming, location analytics and machine learning. Kinetica enables organizations to simplify their infrastructure on a single platform and eliminate data movement, and costly integration with additional graph solutions. As a result, Kinetica can power transformational enterprise use cases such as vehicle routing, matching supply and demand, and fraud detection that take advantage of built in graph solvers and powerful querying capabilities along with real-time streaming data, geospatial analysis, and predictive models.

IN THIS TECH TALK WE WILL COVER:

• An introduction to graph analytics
• The value of converging graph analytics with streaming, historical, and location analytics as well as machine learning with Kinetica
• Real-world use cases and non-traditional applications of graph analytics
• A demo of Kinetica’s converged graph capabilities

The post An Introduction to Graph Analytics With The Kinetica Streaming Data Warehouse appeared first on Kinetica - The Real-Time Database.

Do you want to keep your data warehouse running no matter what? Do you want to reduce latency for a global community of users? In this Tech Talk we’ll explore the advantages of high availability (HA) and global replication to keep systems running all the time. What happens if you lose a data center? We’ll share how you can deliver global applications with disaster recovery and fault tolerance to avoid costly outages and business disruptions. Reliable mission-critical analytical applications are important for any organization. Whether it’s real-time financial risk monitoring, on-demand inventory replenishment, or dynamic delivery truck routing; having reliable performance is critical for any data warehouse

In this Tech Talk we will cover:

• An overview of a high availability architecture
• Implementing on premise or in the cloud
• Improving latency for global applications
• Configuring high availability with Kinetica (Active-Active, Active-Passive, N+1)
• Discussion of real-world use cases of HA
• A demo of Kinetica HA

The post Resilient and Global Active Analytics – An Introduction to High Availability with Kinetica appeared first on Kinetica - The Real-Time Database.

We will cover:

• Powerful Kinetica text search capabilities
• How to use Kinetica full text search
• Demo real-time geospatial data using full text search
• Applying text search to real-time data feeds for feature extraction to train models

The post How to Combine Text Search, Geospatial and Machine Learning Techniques for Streaming Data Analysis appeared first on Kinetica - The Real-Time Database.

As an app developer, it’s exciting to create innovative apps that enable the business to achieve things that were previously impossible. If you are looking at Kinetica, it’s likely you are being asked to scale applications working with streaming, geospatial and machine learning. In this session, we’ll show you how easy it is to build these kinds of applications on top of the Kinetica Streaming Data Warehouse.

In this session we’ll cover:

• When requirements for “Smart Applications” call for “Active Analytics”
• How Kinetica keeps you out of the stack administration business
• Overview of common types of applications for Kinetica: Geospatial and ML
• Tools of the Trade: SQL, APIs, UDFs, Graph, Mapping, and Charting
• Demo applications for Geospatial and Machine Learning

The post Developing Smart Analytical Apps With Kinetica appeared first on Kinetica - The Real-Time Database.

For retailers, ensuring products are always in stock is a top priority. However, with the complexity of the modern supply chain and the sheer number of SKUs involved, this is a significant logistical challenge. Retailers need to be able to analyze data from stores in real-time and adjust inventory to meet customer demand. Kinetica works with one of the world’s largest retailers to power dynamic inventory replenishment to ensure their customers always have what they need.

IN THIS TECH TALK WE’LL DISCUSS:

• Challenges to delivering dynamic inventory replenishment at scale
• Key components of a data-driven replenishment solution
• An example of how active analytics enables real-time analysis of streaming point of sale data to power inventory replenishment

The post Active Analytics for Dynamic Inventory Replenishment appeared first on Kinetica - The Real-Time Database.