In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.
Percona Previews Open Source Database as a Service, Enables Choice to Run Anywhere
Percona, a leader in open source database software and services, announced a preview of its 100% open source Database as a Service (DBaaS), which eliminates vendor lock-in and enables users to maintain control of their data. As an alternative to public cloud and large enterprise database vendor DBaaS offerings, this on-demand self-service option provides users with a convenient and simple way to deploy databases quickly. Plus, using Percona Kubernetes Operators means it is possible to configure a database once, and deploy it anywhere.
“The future of databases is in the cloud, an approach confirmed by the market and validated by our own customer research,” said Peter Zaitsev, co-founder and CEO of Percona. “We’re taking this one step further by enabling open source databases to be deployed wherever the customer wants them to run – on-premises, in the cloud, or in a hybrid environment. Companies want the flexibility of DBaaS, but they don’t want to be tied to their original decision for all time – as they grow or circumstances change, they want to be able to migrate without lock-in or huge additional expenses.”
Altair One Cloud Platform Delivers Most Advanced Environment for Collaborative, Data-driven Design and Development
Altair (Nasdaq: ALTR), a global technology company providing solutions in simulation, high-performance computing (HPC), and artificial intelligence (AI) announced general availability for a host of new features and functionality in Altair One, a fully integrated platform that brings together the company’s entire product suite and HPC capabilities to facilitate seamless collaboration and faster time-to-market. Eliminating the boundaries between computer-aided engineering (CAE) and data analytics, Altair One delivers access to a unified development environment and offers multi-disciplinary teams the on-demand HPC critical to complete complex projects quickly and efficiently.
“Altair One provides a modern, single pane of glass approach to leverage HPC and cloud resources for running computational science applications anywhere and everywhere at scale, which is key to optimizing outcomes and achieving faster time-to-value,” said James R. Scapa, founder and chief executive officer, Altair. “With the launch of Altair One, we are empowering our customers with all the software and tools to seamlessly manage hybrid on-premises and cloud HPC resources to process workloads in the most cost-efficient and fastest way possible.”
SensiML Launches Open Source Initiative to Drive TinyML Implementations for Smart IoT Applications
SensiML™ Corporation, a leading developer of AI tools for building intelligent Internet of Things (IoT) endpoints, announced that it has launched an Open Source Initiative to accelerate the adoption of TinyML smart sensing IoT applications. The initiative builds upon SensiML’s existing efforts to design flexibility, transparency, and efficiency into its product suite by giving developers control and insight over vital aspects of their ML workflow, tools, data, and resulting models. SensiML’s Open Source Initiative is the next logical step in the company’s ongoing commitment to provide its embedded developers and partners with the confidence to build AI code into supportable, commercial IoT products.
“Our industry-leading TinyML development software for creating smart IoT sensing applications and devices uses AutoML technology to allow developers to leverage ML with or without data science expertise,” said Chris Rogers, CEO of SensiML. “With the introduction of SensiML’s Open Source Initiative, we are making it easier than ever for developers to adopt AI at the IoT edge by removing two key barriers of unexplainable code behavior and unmodifiable AutoML firmware output from such tools.”
Brightflag Sets Standard for Legal AI, Invests 100,000 Hours in Machine Learning Model
Brightflag, the AI-powered enterprise legal management company, surpassed a significant product milestone with 100,000 hours now invested in the development and training of its proprietary machine learning model by its in-house team of data science and corporate legal experts. The insights generated by Brightflag’s AI have been validated and applied by hundreds of corporate legal teams while managing live legal matters.
“AI is the root of our technology, not just a single branch, so it’s validating to see the growing consensus among legal operations professionals and legal tech providers around AI’s role in the modern workplace,” said Brightflag CEO Ian Nolan. “For AI to adequately assist legal departments it must be constantly learning. Our investment in AI is the equivalent of more than 10,000 hours of practice every single year, compounded over seven years.”
Octopai Introduces Data Lineage XD, First Solution on the Market to Provide Advanced, Multidimensional Views of Data Lineage
Octopai, a leader in metadata management automation, announced that they are introducing Data Lineage XD, taking data lineage to the next level by providing the first advanced, multidimensional data lineage platform on the market. With Octopai’s Data Lineage XD, enterprises have a complete, in-depth view of data flow, enabling them to drive more value from their data assets.
“Corporations manage an abundance of data every day and many business decisions are dependent on that information. We are seeing more than ever that data access no longer resides in the Business Intelligence department alone; executives throughout a corporation need access to accurate data, fast,” says Amnon Drori, CEO of Octopai. “Our platform addresses the needs of each data user, providing a complete data lineage solution at every level. Our customers feel that with our platform they are getting a premium platform at an affordable price.”
AIStorm’s AI-in-Imager Solutions Use Tower Semiconductor’s Hi-K VIA Capacitor Memory To Enable High-Density Imager, Always-On Processing
AIStorm and Tower Semiconductor announced that AIStorm’s new AI-in-imager products will feature AIStorm’s electron multiplication architecture and Tower’s Hi-K VIA capacitor memory, instead of digital calculations, to perform AI computation at the pixel level. This saves on the silicon real estate, multiple die packaging costs, and power required by competing digital systems—and eliminates the need for input digitization. The Hi-K VIA capacitors reside in the metal layers, allowing the AI to be built directly into the pixel matrix without any compromise on pixel density or size.
“This new imager technology opens up a whole new avenue of always-on functionality. Instead of periodically taking a picture and interfacing with an external AI processor through complex digitization, transport and memory schemes, AIStorm’s pixel matrix is itself the processor & memory. No other technology can do that,” said Dr. Avi Strum, SVP of the sensors and displays business unit at Tower Semiconductor.
Eko’s AI Analysis Algorithm Validated as a Clinical Tool for Detecting Heart Murmurs
Eko, a cardiopulmonary digital health company, today announced the peer-reviewed publication of a clinical study that found that the Eko artificial intelligence (AI) algorithm for detecting heart murmurs is accurate and reliable, with comparable performance to that of an expert cardiologist. The findings suggest utility of the FDA-cleared Eko AI algorithm as a frontline clinical tool to aid clinicians in screening for cardiac murmurs that may be caused by valvular heart disease.
“When it comes to listening for heart murmurs, the standard of care involves a lot of subjectivity on the part of the listener,” said John Maidens, PhD, head of Data Science at Eko. “It takes expert clinicians many years to master the art of hearing and interpreting heart murmurs, and there is still a lot of variability. Our study demonstrated considerable variability even among our expert cardiologists.”
Domino Data Lab Launches ‘Data Science Leaders’ Podcast with Insights on Enterprise MLOps
Domino Data Lab, provider of a leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, announced the debut of ‘Data Science Leaders,’ a new podcast for data science teams featuring insights from top data and analytics executives at the world’s most impactful companies. The weekly podcast series kicks off today, with the first episode available online and on Apple Podcasts, Spotify, Google Podcasts, and Stitcher.
“Many of today’s best data science leaders have pioneered new model-based methods for building competitive advantage and solving the world’s most important problems,” said Nick Elprin, CEO and co-founder at Domino Data Lab. “We’re honored to present this podcast to help the next generation of data science leaders learn proven steps they can take to enable model-driven business at scale.”
NEC Develops Neuroscience-inspired AI Technology for Time Series Analysis
NEC Corporation (NEC; TSE: 6701) announced the development of artificial intelligence (AI) technology that makes high-speed decisions while maintaining high accuracy in real-time analysis of time series data. This technology is expected to enable face recognition as well as cyberattack detection and analysis to be accelerated by up to 20 times while maintaining the same accuracy as existing methods.
Typical AI engines for face recognition and cyberattacks depend on a preset amount of data to be collected before making a decision. For example, at entrance gates that utilize face recognition, individuals are authenticated by taking a previously fixed number of frames in succession, followed by a final decision.
NEC’s new technology collects and analyzes data without a previously fixed amount of data required. Inspired by neuroscience, the technology makes a decision as quickly and accurately as possible by accumulating evidence until a certain confidence level (likelihood) is reached. Since additional data collection is unnecessary after reaching the desired confidence level, computations can be accelerated when compared to conventional approaches.
dotData Launches dotData Py Lite, Putting the Power of AI Automation on Every Data Scientist’s Laptop
dotData, a pioneer in AI automation and operationalization for the enterprise, announced the launch of dotData Py Lite, a new containerized AI automation solution to enable data scientists to execute quick POCs and deploy dotData on their desktop. Designed for Python data scientists, dotData Py Lite offers dotData’s award-winning automated feature engineering and automated machine learning (ML) in a portable environment, allowing data scientists to explore 100x more features, augment their hypotheses, and improve their ML models quickly without having to rely on large and expensive enterprise-AI environments.
“Great machine learning algorithms do not guarantee great AI models — the secret is feature engineering. Whether using machine learning for product demand forecasting, customer churn, revenue recovery, or failure detection, building strong features is difficult but critical to developing accurate predictions,” said Ryohei Fujimaki, Ph.D., founder and CEO of dotData. “dotData Py Lite was created to put the power of enterprise-grade automated feature engineering on everyone’s laptop. It takes one minute to install, ten minutes to develop, and deploys instantly.”
Aerospike Announces Strategy Giving Enterprises Instant Access to Any Data at Petabyte Scale
Aerospike Inc. unveiled an innovation strategy that leverages the scale and performance of its real-time data platform to enhance and streamline query capabilities on massive data sets. The strategy and first series of innovations—revealed during Aerospike Digital Summit—remove the bottlenecks from today’s traditional data architectures that struggle to keep up with data ingest at the edge and artificial intelligence (AI) and machine learning (ML) model drift due to managing data across multiple disparate systems.
“AI and ML applications have an insatiable appetite for data, but traditional databases can struggle to process and combine both streaming and system of record data and to deliver the predictable performance needed for the best decisions,” said Srini Srinivasan, chief product officer and founder, Aerospike. “Today’s enhancements represent a continued expansion of the Aerospike database platform to build upon our strength of acting in real time upon billions of transactions and make it even easier to build and deploy applications for real-time inference-based decisions with lower server footprint.”
YugabyteDB 2.7 Now Generally Available, Extending Multi-Cloud and Kubernetes Support
Yugabyte, a leading open source distributed SQL database, announced the general availability of YugabyteDB 2.7. The latest release offers the most comprehensive set of deployment options for organizations looking to scale distributed SQL across hybrid cloud environments using Kubernetes platforms like Red Hat OpenShift and VMware Tanzu. With support for public and cloud-native environments, Yugabyte enables organizations to realize their strategic Kubernetes, distributed SQL, and microservices initiatives while avoiding cloud lock-in.
“Most organizations are well down the path of adopting Kubernetes, but continue to deploy monolithic databases that can’t take advantage of the elasticity of the cloud,” said Karthik Ranganathan, Co-Founder and CTO at Yugabyte. “To a developer, YugabyteDB 2.7 looks and feels like PostgreSQL, so it’s familiar and easy to develop against. Unlike traditional SQL databases, administrators and operators can scale YugabyteDB on demand, across any infrastructure, and without downtime or operational complexity.”
Kyndi Launches Game-changing Cognitive Search Solution, Enabling Business Users to Quickly Obtain Accurate Answers to Natural Language Question
Kyndi, Inc., an industry leader in delivering next-generation AI solutions for business users, announced an intelligent search product powered by its proprietary cloud AI platform. The new Spring 2021 release of Kyndi Cognitive Search Platform is designed to understand the true intent of a business user inquiry against documents, emails, manuals, or other forms of textual data to return contextually relevant answers. Providing dramatic time and cost-savings for companies accustomed to needing long and expensive machine learning cycles to train models on business-specific language, the Kyndi platform can easily interpret industry-specific terminology, acronyms and synonyms without any data training or AI skills.
According to Ryan Welsh, CEO and founder of Kyndi, Inc., “Unlike traditional AI search tools that aren’t intuitive and provide only limited, keyword-based results, the Kyndi platform augments humans by mimicking the human thought process. Employees may ask questions of data a dozen ways that all mean the same thing. But Kyndi has the context-driven intelligence to not only understand the intent of a user’s question, but also to explain — at a granular level — why an answer was returned. And this is exactly where traditional search techniques fail to deliver the experience that business users are looking for.”
Epiq Accelerates Artificial Intelligence (AI) Innovation for the Legal Industry
Epiq, a global technology-enabled services leader for corporate legal departments and law firms, announced that it is providing a unique combination of pre-built AI models, a team of experts, and an innovative program to help its Epiq eDiscovery Managed Services clients create, build, and nurture their own AI model libraries based on their specific practices and data sets. Epiq’s dedicated eDiscovery Managed Services organization has long led the industry with its programmatic and tailored approach to eDiscovery and service delivery innovation. Its application of advanced AI technology within a proven eDiscovery managed services delivery model makes Epiq the first to offer law firms and corporate legal departments managed AI services—including unlimited access to industry-leading AI platforms such as Brainspace and NexLP from Reveal, a global provider of the leading AI-powered eDiscovery platform.
“With our AI models library program, our goal is to empower our clients to harness AI to create sustainable value that is distinctive to their organizations,” said Roger Pilc, president of Epiq Legal Solutions. “This differs from the common industry approaches of many service providers and emerging SaaS vendors, which range from not helping clients harness cumulative learning from their projects, to leveraging client data to train vendor AI models and yield benefits for the vendor, rather than for the client.”
ThoughtSpot Everywhere Launches as Low-Code Platform to Build Interactive Data Apps with Search & AI-driven Analytics
ThoughtSpot, the Modern Analytics Cloud company, announced the launch of ThoughtSpot Everywhere. ThoughtSpot Everywhere is the first low-code embedded analytics platform that allows developers and product leaders to build interactive data apps and incorporate any service available in the Modern Analytics Cloud, including search and AI-driven analytics, directly into their apps, products, and services. In doing so, companies can give their customers and partners access to the entire modern data stack through consumer-grade analytics.
“Our customers have shown us the incredible possibilities that can be realized when they empower their teams to answer their own data questions and make decisions. With ThoughtSpot Everywhere, we’re helping our customers unleash the same potential for their own customers and users,” said Sudheesh Nair, CEO, ThoughtSpot. “ThoughtSpot Everywhere’s low-code, API-approach makes it simple for our customers to take advantage of the entire modern analytics cloud. It’s never been easier to build the most modern, simple, and powerful data experiences in your own offerings to delight customers and scale automation.”
Hewlett Packard Enterprise expands HPE GreenLake with breakthrough storage as-a-service business transformation
Hewlett Packard Enterprise (NYSE: HPE) announced innovations that transform HPE Storage into a cloud-native, software-defined data services business. As part of today’s news, HPE unveiled a data services platform that delivers on its Unified DataOps vision for a new data experience that brings a cloud operations model to wherever data lives and unifies data operations. The new platform is designed to address the data explosion edge-to-cloud, collapse the silos and complexity that plague data environments, maximize agility and innovation, and reduce business risk. Today’s announcement marks an important milestone in HPE’s vision to become an edge-to-cloud platform as-a-service company. The new data services platform, available through HPE GreenLake, consists of three new innovations that simplify data operations from edge to cloud.
“Organizations face a complex web of fragmented hardware, software, and manual processes, making it difficult for them to compete and innovate in a constantly changing marketplace,” said Antonio Neri, President and CEO, HPE. “HPE was the first to recognize the need to deliver a unified and consistent cloud experience, from edge to cloud, with HPE GreenLake. Today’s announcement builds on this strategy, by enabling our customers to break down silos and leverage data, wherever it resides, with unified data operations. As we enter the Age of Insight, HPE is providing the ideal platform for organizations seeking to apply distributed data to fuel AI initiatives, deliver new customer experiences, and drive digital transformation.”
Hitachi Vantara Enhances Lumada Portfolio to Deliver Improved Resilience, Agility and Accuracy in Industrial IoT Environments
Hitachi Vantara, the digital infrastructure, data management, and digital solutions subsidiary of Hitachi, Ltd. (TSE: 6501), announced advancements to the Lumada software platform and industry solutions to accelerate the digital transformation of industrial processes. These offerings help deliver real-time, actionable insights that accelerate the ability to predict problem areas, streamline production and maintenance, and create a connected supply chain – resulting in operational efficiency, minimal revenue disruptions, and product quality improvements.
Improving manufacturing operational outcomes involves comprehensive data analysis and integration from thousands of moving parts across remote and industrial environments. Lumada is Hitachi’s digital platform that connects data, assets, and people to fuel industry innovation. It is the software foundation for Lumada Industry Solutions, that extract data-driven insight and drive better operational and business outcomes. The updated Lumada portfolio allows customers to automate tasks and make faster decisions by training data models in the cloud and deploying them to edge devices, creating actionable insights from diverse data sets at lower infrastructure cost.
“Across the globe, industries are dealing with increasing complexity, a faster changing environment and greater competition that together are driving a need for accelerated digitalization. Supply chain disruptions, health and safety measures and operational challenges have highlighted this need for data-driven innovation,” said, Radhika Krishnan, Chief Product Officer, Hitachi Vantara. “Today’s advancements allow our customers to make faster, more informed decisions so industries can thrive in our rapidly digitalizing future.”
Algolia Launches Algolia Recommend — A New API-First Product to Generate Blazing Fast, Highly Relevant Recommendations at Scale for Retailers
Algolia, a leading API Platform for Dynamic Experiences, launched Algolia Recommend: a high-performing, Artificial Intelligence (AI)-optimized API that accelerates the creation and implementation of product or content recommendations across digital touchpoints.
Algolia Recommend surfaces in milliseconds the most relevant recommendations, offers, or suggestions for a shopper using Machine Learning models that collect data from two sources: shopper behavior (the shoppers’ actions across a website or app, including previous purchases) and product data (all product attributes contained in the product catalogue, including product, description, availability, and price).
“Algolia recently unveiled its new company direction and vision and helped customers go beyond the search box with their digital commerce strategies,” said Julien Lemoine, co-founder and chief technology officer of Algolia. “The release of Algolia Recommend provides the next building block for retailers to optimize their online experience and increase their revenue. These retailers have already unlocked $1 billion+ additional annual revenue on the back of up to 1.7 trillion searches across Algolia’s API platform.”
BigID Reimagines Data Management With The First Open, Extensible App Store & Marketplace for Data Privacy, Security and Governance
BigID, a leading data intelligence and management platform for privacy, protection, and perspective, announced the release of the BigID App Marketplace, designed to provide BigID customers with modular add-on apps for data privacy, protection, and perspective – in a unified data management platform. BigID’s app marketplace makes it easy for organizations to get more value from their data discovery, catalog and classification by easily adding apps for actioning data in data compliance, security or governance. Apps in the BigID App Marketplace can be purchased or downloaded on-demand and provide an easy way for enterprises to expand BigID data management. Apps are developed by BigID and 3rd party vendors.
“Data drives business – but there’s no one size fits all solution. That’s why we created the first app marketplace for data intelligence,” said Dimitri Sirota, CEO of BigID. “Enterprises can now leverage the deep data insight of BigID’s data discovery to extend and enrich to apps across their entire tech stack – they can build their own, bring their own, or buy apps to get value from their data for privacy, security, and governance.”
Modern BI Platform Toucan Toco Launches Native Integration with Snowflake
Data storytelling pioneer Toucan Toco announced the launch of a new suite of features natively built for Snowflake, designed to help organizations seamlessly and securely harness the full power of their cloud data. With a simple one-click connection to Snowflake, Toucan empowers teams to rapidly unlock insights and value from their cloud data at scale, with no need for laborious onboarding or data duplication, delivering a 40% cost reduction over conventional business intelligence (BI) tools, should they do the same.
“At Toucan, we’re committed to delivering best-of-breed data storytelling, and our native features for Snowflake are a key step toward delivering the scalable, integrated solutions that global enterprises need,” says Charles Miglietti, Toucan’s co-founder and CEO. “We’re facilitating the migration to the cloud by giving business users instant access to their data, with no need to duplicate data or overhaul permissions structures, to deliver faster and more cost-effective results for enterprises of all kinds.”
Appen Leads Industry in Creating AI That Works for Everyone
Appen Limited (ASX:APX), a leading provider of high-quality training data for organizations that build effective AI systems at scale, is enabling organizations to launch, update and operate unbiased AI models through a range of projects and partnerships. With support from the company’s global crowd of data annotation specialists that’s more than a million strong, Appen has developed diverse training data sets for AI models, particularly natural language processing (NLP) initiatives to ensure end users receive the same experience, no matter their language variety, dialect, ethnolect, accent, race or gender.
“The quality and diversity of training data directly impacts the performance and bias present in AI models,” said Appen CEO Mark Brayan. “As a data partner, we can supply complete training data for many use cases to ensure AI models work for everyone. It’s critical that we engage a diverse group of individuals to produce, label, and validate the data to ensure the model being trained is not only equitable, but also built responsibly.”
Explorium Launches Signal Studio for Fast, Curated External Data Acquisition
Explorium, the automated external data platform for advanced analytics and machine learning, today announced the launch of Signal Studio, a product that enables data and business analyst teams to quickly find and integrate the most relevant external data signals to their analytics pipelines. Signal Studio automates the data acquisition process, dramatically reducing discovery costs and ensuring businesses invest in datasets with optimal relevance, accuracy and compliance.
“Any enterprise leveraging analytics and data science will get a competitive edge by building scalable strategies to find, understand and leverage data outside of their organization,” said Omer Har, CTO of Explorium. “With the launch of Signal Studio, the Explorium platform automates the entire lifecycle of using external data for analytics: from discovery, acquisition, preparation and pipeline integration to internal data augmentation, model deployment and insight delivery.”
Dynamic Yield’s Deep Learning Product Recommendations Generate Exponential Revenue Returns
Dynamic Yield, the Experience Optimization platform, announced the gradual release of its state-of-the-art, self-training Deep Learning Recommendations Algorithm, enabling brands to predict the next series of products a consumer is most likely to buy.
Today, product recommendations are an essential requirement for any eCommerce business looking to increase engagement, purchases, and loyalty. However, a consistent challenge for marketers and merchandisers has been determining which products among a massive catalog of items to serve customers with various preferences and levels of intent.
Dynamic Yield’s Deep Learning-Based Recommendations instantly identify intent, even from the first session, to automatically match customers with the products they are most interested in or likely to buy, adapting as new data is ingested. The model employs the item2vec method, derived directly from its Natural Language Processing (NLP) counterpart, word2vec, to learn the products in a user’s browsing history, in-session activity, and trends seen across the site to recommend products each individual is predicted to engage with as they shop.
“Consumers have come to expect a high level of personalization in online retail interactions,” said Liad Agmon, CEO of Dynamic Yield. “Our Deep Learning model exploits cutting-edge neural network technologies of natural language processing that are found to be extremely effective within the recommendations domain, providing a superior approach for predicting customer wants and needs.”
Fivetran Expands “Powered by” Managed Service to Fuel New Data Products
Fivetran, a leading automated data integration provider, announced new functionalities and a significant expansion of Powered by Fivetran, a managed service offering that gives cloud application providers and data insights companies a faster, more efficient way to build custom data-powered experiences on top of the Fivetran infrastructure.
“Powered by Fivetran solves a data problem faced by many product teams, data consultancies and marketing agencies — how do we easily and securely connect to data from our customers, partners and other external groups,” said Alexa Maturana-Lowe, Director of Product Management at Fivetran. “The Powered by Fivetran offering remains a major focus for our product team. We’re proud to evolve it with new functionalities that will help us better serve our customers and save them valuable time.”
Veezoo Unlocks the Power of Data Analytics for Everyone
What if you could just ask? That’s the question the founders of Swiss data analytics startup Veezoo set out to answer during a 2015 hackathon. Frustrated by their own experiences using queries to derive meaningful analytics from enterprise databases, the team set out to develop a human-centric approach to extract useful business intelligence from any database. Their initial proof of concept won the hackathon, and Veezoo was born.
Six years later, their initial concept has evolved into a sophisticated ‘Siri for enterprises’ that turns natural-language queries of a cloud database into insightful answers in the form of text, data tables, and rich visualizations. Veezoo’s ‘connect once, explore everything’ philosophy empowers sales, marketing, and operations teams to get instant answers to complex questions, understand and compare dynamic trends, and derive insightful business analytics—all without hiring a data scientist or asking the IT department to implement a costly business intelligence module.
“Today’s business intelligence systems are frustratingly dumb and unintuitive. They feature complicated 1990s drag-and-drop interfaces that confuse non-technical users,” said Marcos Monteiro, co-founder and CEO of Veezoo. “We wondered why nobody had created an intelligent system to make it easy for people without a degree in computer science to get the answers they need from cloud data warehouses. So, we decided to build one.”
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