Strata Data Awards
The Strata Data Awards recognize the most innovative startups, leaders, and data science projects from Strata sponsors and exhibitors around the world. Curated by a team of industry experts and selected by the attendees of the world’s largest data conference series, the Strata Data Award is given to the most disruptive startup, the most innovative industry technology, the most impactful data science project, and the most notable open source contribution.
Voting for the Strata Data Awards will take place in the Expo Hall, starting Tuesday night, September 24, 5:00pm–7:00pm, and running all day Wednesday, September 25, 11:00am–7:00pm.
Each finalist will have a sign at their booth specifying the general voting rules and their own individual text-to-vote code.
Attendees simply text the unique code of the finalists they choose to a text number we will provide once voting is open.
After voting ceases at 7:00pm on Wednesday, the results will be tallied, and the winners will be announced onstage during Thursday keynotes.
A project or initiative that uses data science and analytics to have a widespread, positive impact on society
Using AI to detect and prevent the dissemination of online terrorist propaganda content for the UK Home Office.
KNIME Image Processing Extension for Biomedical Image Analysis: Analysis of Human Tissue Cells to Aid Diagnosis of Aggressive Cancer for the Vanderbilt University Medical Center, School of Medicine, Zijlstra Lab.
- Micro Focus (Vertica)
Climate FieldView digital agriculture platform for innovative farming for the Climate Corporation.
A new, private company less than four years old, with fewer than 100 employees and under $50M in raised capital
Cryptonumerics solutions are a new generation of privacy automation software, leveraging state-of-the-art technologies, including machine learning metadata classification, automated risk assessment for reidentification, advanced privacy protection actions, and secure multiparty computation.
Imply is an “open core” company that offers a real-time analytics solution based on Apache Druid (incubating), an open source database that can ingest millions of records per second and deliver subsecond response to queries across billions of rows. Independent query speed benchmarks show Druid outperforming Apache Hive by up to 190 times and Presto by up to 59 times. Druid integrates natively with Kafka, Kinesis, S3, HDFS, and more.
Timescale develops TimescaleDB, the easiest, fastest, and most reliable place to store and analyze time series data. Businesses all over the world trust TimescaleDB for powering mission-critical applications, including industrial data analysis, complex monitoring systems, financial risk management, and geospatial asset tracking.
- Amazon Web Services
AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. A data lake enables you to break down data silos and combine different types of analytics to gain insights and guide better business decisions.
Personalizer is a cloud-based AI service that helps you deliver experiences unique to each user, learning from their real-time behavior. Your client application provides a list of possible actions, with information about them and information about the context, which may include information about the user, device, time, etc.
- Attunity (Qlik)
Attunity Compose for Data Lakes automates data pipelines to create and continuously update analytics-ready data stores for cloud and on-premises data lakes. By automating data ingestion, Spark transforms and creation of analytic-ready, enterprises realize faster data lake value and ROI.
- Plotly Dash
Dash is an open source library compatible with Python and R for creating analytic web apps. It links interactive UI controls and displays (sliders, dropdown menus, graphs) to your data analytics code, giving you hands-on input.
- Apache Flink
Apache Flink is an advanced stateful stream processing framework taking a unified approach to batch and streaming data processing. It sets out to provide “streaming first, with batch as a special case of streaming” as a powerful paradigm to reduce the complexity of data infrastructures and build data applications that generalize across real-time and offline processing.
From its inception, MinIO was designed to deliver high-performance, S3 compatible, object storage for the enterprise. The project founders recognized that as data growth accelerated, traditional storage solutions would not scale effectively. Further, most legacy object storage architectures had not taken advantage of the valuable learning undertaken by the hyperscalers—ultimately placing a ceiling on their performance.
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