BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT
Comet | November 17, 2022
Comet, provider of the leading MLOps platform for machine learning (ML) teams from startup to enterprise, today announced a bold new product: Kangas. Open sourced to democratize large scale visual dataset exploration and analysis for the computer vision and machine learning community, Kangas helps users understand and debug their data in a new and highly intuitive way. With Kangas, visualizations are generated in real time; enabling ML practitioners to group, sort, filter, query and interpret their structured and unstructured data to derive meaningful information and accelerate model development.
Data scientists often need to analyze large scale datasets both during the data preparation stage and model training, which can be overwhelming and time-consuming, especially when working on large scale datasets. Kangas makes it possible to intuitively explore, debug and analyze data in real time to quickly gain insights, leading to better, faster decisions. With Kangas, users are able to transform datasets of any scale into clear visualizations.
“A key component of data-centric Machine Learning is being able to understand how your training data impacts model results and where your model predictions are wrong. “Kangas accomplishes both of these goals and dramatically improves the experience for ML practitioners.”
Gideon Mendels, CEO and co-founder of Comet
Putting Large Scale Machine Learning Dataset Analysis at Your Fingertips
Developed with the unique needs of ML practitioners in mind, Kangas is a scalable, dynamic and interoperable tool that allows for the discovery of patterns buried deep within oceans of datasets. With Kangas, data scientists can query their large-scale datasets in a manner that is natural to their problem, allowing them to interact and engage with their data in novel ways.
Noteworthy benefits of Kangas include:
Unparalleled Scalability: Kangas was developed to handle large datasets with high performance.
Purpose Built: Computer Vision/ML concepts like scoring, bounding boxes and more are supported out-of-the-box, and statistics/charts are generated automatically.
Support for Different Forms of Media: Kangas is not limited to traditional text queries. It also supports images, videos and more.
Interoperability: Kangas can run in a notebook, as a standalone local app or even deployed as a web app. It ingests data in a simple format that makes it easy to work with whatever tooling data scientists already use.
Open Source: Kangas is 100% open source and is built by and for the ML community.
Kangas was designed for the entire community, to be embraced by students, researchers and the enterprise. As individuals and teams work to further their ML initiatives, they will be able to leverage the full benefits of Kangas. Being open source, all are able to contribute and further enhance it as well.
“Interoperability and flexibility are inherent in Comet’s value proposition, and Comet aims to expand on that value through open source contributions,” added Mendels. “Kangas is a continuation of all of our efforts, and we couldn’t wait to get its capabilities into the hands of as many data scientists, data engineers and ML engineers as possible. We believe by open sourcing it, Comet can help teams get the most out of their ML projects in ways that have not been possible previously.”
Kangas is available as an open source package for any type of use case. It will be available under Apache License 2 and is open to contributions from community members.
Comet provides an MLOps platform that data scientists and machine learning teams use to manage, optimize, and accelerate the development process across the entire ML lifecycle, from training runs to monitoring models in production. Comet’s platform is trusted by over 150 enterprise customers including Affirm, Cepsa, Etsy, Uber and Zappos. Individuals and academic teams use Comet’s platform to advance research in their fields of study. Founded in 2017, Comet is headquartered in New York, NY with a remote workforce in nine countries on four continents. Comet is free to individuals and academic teams. Startup, team, and enterprise licensing is also available.
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA ARCHITECTURE
Mode Analytics | September 30, 2022
Mode Analytics today announced that it has been recognized as a Business Intelligence Leader in the inaugural Modern Marketing Data Stack Report: Your Technology Guide to Unifying, Analyzing, and Activating the Data that Powers Amazing Customer Experiences, executed and launched by Snowflake, the Data Cloud company.
Snowflake’s data-backed report identifies the best of breed solutions used by Snowflake customers to show how marketers can leverage the Snowflake Data Cloud with accompanying partner solutions to best identify, serve, and convert valuable prospects into loyal customers. By analyzing usage patterns from a pool of nearly 6,000 customers, Snowflake identified six technology categories that organizations consider when building their marketing data stacks. These categories include:
Integration & Modeling
Identity & Enrichment
Activation & Measurement
Data Science & Machine Learning
Focusing on companies that are active members of the Snowflake Partner Network (or ones with a comparable agreement in place with Snowflake), as well as Snowflake Marketplace Providers, the report explores each of these categories that comprise the Modern Marketing Data Stack, highlighting technology partners and their solutions as “leaders” or “ones to watch” within each category. The report also details how current Snowflake customers leverage a number of these partner technologies to enable data-driven marketing strategies and informed business decisions. Snowflake’s report provides a concrete overview of the partner solution providers and data providers marketers choose to create their data stacks.
“Marketing professionals continue to expand their investment in analytics to improve their organization’s digital marketing activities. “Mode has emerged as a leader in the Modern Marketing Data Stack, with joint customers leveraging their technology to interpret insights that lead to informed business decisions.”
Denise Persson, Chief Marketing Officer at Snowflake
Mode was identified in Snowflake’s report as a Leader in the Business Intelligence category for its particular success with Visual Explorer, Mode’s flexible visualization system that helps analysts explore data faster and provides easy-to-interpret insights to business stakeholders. Additionally, Mode and Snowflake have partnered in the past couple of years tocreate a modern data analytics stack, mobilizing the world’s data with the Snowflake Data Cloud to help joint customers quickly execute queries and perform analysis.
“Mode combines the best elements of business analytics and data science into a single platform, unlocking new ways for marketers to accelerate data-driven outcomes,” said Gaurav Rewari, CEO, Mode Analytics. “Our partnership with Snowflake makes it possible for marketing and other departments across an organization to truly centralize and interact directly with their data. With Snowflake’s single, integrated data platform, built to fully leverage the speed and flexibility of the cloud, organizations can mobilize their data in near-real time.”
About Mode Analytics
Mode’s advanced analytics platform is designed by data experts for data experts. It allows data scientists and analysts to visualize, analyze, and share data using a powerful end-to-end workflow that covers everything from early data exploration stages to presentation-ready shareable products. Unlike traditional business intelligence tools that produce static dashboards and reports, Mode brings the best of BI and data science together in a single platform, empowering everyone at your organization to use data to make high quality, high velocity decisions. Mode also supports the analytics community with free learning resources such as SQL School, open source SQL queries, and free tools for anyone analyzing public data.
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT
Neo4j | November 09, 2022
Neo4j®, the leader in graph technology, announced today the general availability of Neo4j 5, the next-generation cloud-ready graph database. Neo4j 5 widens the performance lead of native graphs over traditional databases while providing easier scale-out and scale-up across any deployment, whether on-premises, in the cloud, hybrid, or multi cloud. The result empowers organizations to more quickly create and deploy intelligent applications at large scale and achieve greater value from their data.
"Graph technology adoption is accelerating as organizations seek better ways to leverage connections in data to solve complex problems at scale," said Emil Eifrem, CEO and Co-founder of Neo4j. "We designed Neo4j 5 to deliver the type of scalability, agility, and performance that enable organizations to push the envelope on what's possible for their data and their business."
Neo4j 5's specific benefits include:
Query language improvements and up to 1000x faster query performance. New syntax makes it even easier to write complex pattern-matching queries. Improvements in indexes, query planning, and runtime make Neo4j 5 the fastest implementation ever. For example, multi-hop queries can now be executed up to 1000x faster than Neo4j 4. These improvements are above and beyond the already exponentially faster Neo4j's graph results over traditional databases. Together, these benefits enable more real-time results at scale.
Automated scale-out across hundreds of machines, enabling self-managed customers to grow and handle a massive number of queries with little manual effort and significantly less infrastructure cost. This benefit is achieved via new and enhanced features like Autonomous Clustering and Fabric, enabling organizations to efficiently operate very large graphs and scale out in any environment. Neo4j 5 also automates the allocation and reassignment of computing resources.
Continuous updates across all deployments, whether in the cloud, multi-cloud, hybrid, or on-prem. Neo4j 5 ensures ongoing compatibility between self-managed and Aura workloads managed by Neo4j. In addition, a new tool called Neo4j Ops Manager provides a unified single pane for easy monitoring and management of global deployments, giving customers full control over their environments.
Neo4j 5 performance lead sets a new industry bar
More than 1,300 organizations trust Neo4j's technology to power mission-critical applications while maintaining performance, security, and data integrity. Neo4j 5 extends the company's leadership even further at a time when graph adoption is exploding.
"Switching to Neo4j was a huge win for us," said David Fox, Senior Software Engineer at Adobe and Co-founder & Engineering Lead at devRant. "We've seen significant performance improvements, and a great reduction in complexity, storage, and infrastructure costs. Staff now focus on improving the infrastructure, versus spending time frustratingly micro-managing it."
For more information
To learn more about Neo4j 5, visit the Neo4j 5 web page, read "Scale New Heights with Neo4j 5 Graph Database," or register for the following sessions at the upcoming online developer conference NODES 2022: "What's New in Neo4j 5 and Aura 5 for Developers" and "Introducing Neo4j 5 for Administrators."
Neo4j is the world's leading graph data platform. We help organizations – including Comcast, ICIJ, NASA, UBS, and Volvo Cars – capture the rich context of the real world that exists in their data to solve challenges of any size and scale. Our customers transform their industries by curbing financial fraud and cybercrime, optimizing global networks, accelerating breakthrough research, and providing better recommendations. Neo4j delivers real-time transaction processing, advanced AI/ML, intuitive data visualization, and more.
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT
Treasure Data | October 31, 2022
Treasure Data, an award-winning enterprise customer data platform (CDP), today announced an integration with Amazon Marketing Cloud, a clean room solution by Amazon Ads, to help advertisers better understand audience segments, advertising performance, and streamline insight generation. Treasure Data is the first CDP vendor integrated with Amazon Marketing Cloud.
Marketers are increasingly using CDPs to maintain their first party CRM information. At the same time, it is estimated that 80% of advertisers with media budgets of $1 billion or more plan to utilize clean rooms by 2023. To help advertisers acquire more value from their clean room experiences, this seamless integration between Treasure Data's CDP and Amazon Marketing Cloud, enables enterprises to develop richer and more timely insights for optimized marketing, advertising, and campaign investments.
"As a pioneer in the CDP industry, Treasure Data is honored to be the first customer data platform to have completed this unique integration with Amazon Marketing Cloud. "The integration helps our customers to increase the overall effectiveness of their marketing and advertising campaigns with this global and privacy-safe solution."
John Baudino, Vice President of Partnerships at Treasure Data
Treasure Data customers using Amazon Ads for campaigns will now be able to easily send curated audiences to Amazon Marketing Cloud for enhanced and aggregated and anonymized insights such as audiences' in-market groups, lifestyle cohorts, and brand engagement patterns. The enriched segments derived from Amazon Marketing Cloud insights can then be used to fine-tune audience strategy for an advertiser's Amazon DSP campaigns. Audience insights returned are aggregated and anonymous.
Features enabled through the integrations will be available to customers through the Treasure Data Marketplace.
About Treasure Data
Treasure Data Customer Data Cloud helps enterprises use all of their customer data to improve campaign performance, achieve operational efficiency, and drive business value with connected customer experiences. Our suite of customer data platform solutions integrates customer data, connects identities in unified customer profiles, applies privacy, and makes insights and predictions available for Marketing, Service, Sales and Operations to drive personalized engagement and improve customer acquisition, sales, and retention.