Materialized view

Ahana Joins Leading Members of the Presto® Community at PrestoCon as Platinum Sponsor, Will Share Use Cases and Technology Innovations

Retrieved on: 
Wednesday, December 1, 2021
Cloud, GV, CTO, Batch processing, Amazon, Intel, Technology, Apache, Deep learning, Software as a service, Program, S3, SAN, New, Materialized view, AWS, SQL, Twitter, GLOBE, Linux Foundation, TikTok, Ranger, Uber, Meta, Â, Lux Capital, CA, LinkedIn, CPO, Facebook, Growth, C, Cryptocurrency, Oil, Management

Presto was originally created by Facebook who open sourced and donated the project to Linux Foundations Presto Foundation.

Key Points: 
  • Presto was originally created by Facebook who open sourced and donated the project to Linux Foundations Presto Foundation.
  • PrestoCon is a day-long event for the PrestoDB community by the PrestoDB community that will showcase more of the innovation within the Presto open source project as well as real-world use cases.
  • PrestoCon is the marquee event for the Presto community, showcasing the latest development and use cases in Presto, said Dipti Borkar, Cofounder and Chief Product Officer, Ahana, Program Chair of PrestoCon and Chair of the Presto Foundation Outreach Committee.
  • As a leading member of the Presto community and Linux Foundations Presto Foundation, Ahana is also focused on fostering growth and evangelizing open source Presto.

Eventador Shatters the Barrier Between Streaming Data and Applications

Retrieved on: 
Wednesday, March 18, 2020
Computing, Digital technology, Java platform, Apache Kafka, Enterprise application integration, Free software, Message-oriented middleware, Streaming data, Stream processing, Materialized view

AUSTIN, Texas, March 18, 2020 /PRNewswire-PRWeb/ -- Eventador.io , the streaming data engine for building killer applications, unveiled the Eventador Platform version 2.0, the first end-to-end, produce-to-consume stream processing platform that gives companies the ability to quickly and easily build applications from the firehose of streaming data.

Key Points: 
  • AUSTIN, Texas, March 18, 2020 /PRNewswire-PRWeb/ -- Eventador.io , the streaming data engine for building killer applications, unveiled the Eventador Platform version 2.0, the first end-to-end, produce-to-consume stream processing platform that gives companies the ability to quickly and easily build applications from the firehose of streaming data.
  • With Eventador, data science, developer, and data engineering teams can unlock new value and power from data streams in their models, applications, and ETL flows.
  • "Companies have adopted Apache Kafka as the de facto data bus for streaming data," said Kenny Gorman, Co-founder and CEO of Eventador, "By using the Eventador Platform, customers no longer need to provision expensive, often slow database infrastructure and can instead query streaming data directly using materialized views.
  • The Eventador Platform is a bespoke solution, not a simple add-on to Kafka, that solves the disconnect between streaming data and applications."

Snowflake Announces Automatic Clustering and Materialized Views

Retrieved on: 
Tuesday, November 13, 2018
Computing, Data management, Data, Information technology management, Snowflake, Materialized view, Data warehouse

SAN MATEO, Calif., Nov. 13, 2018 /PRNewswire/ -- Snowflake Computing , the only data warehouse built for the cloud, today announced the immediate availability of two new performance features automatic clustering and materialized views.

Key Points: 
  • SAN MATEO, Calif., Nov. 13, 2018 /PRNewswire/ -- Snowflake Computing , the only data warehouse built for the cloud, today announced the immediate availability of two new performance features automatic clustering and materialized views.
  • "By adding automatic clustering and materialized views, we'll get the answers we and our customers need even faster.
  • Snowflake's automatic clustering provides the following benefits:
    Automated and optimized self-organization of data storage, removing the burden of manually re-clustering data
    Merging and dropping data, and closing gaps between data, which Snowflake manages automatically and in the background
    Snowflake's materialized views provide the following benefits:
    Together, Snowflake's automatic clustering and materialized views deliver faster performance with little to no manual effort, unlike the cost and maintenance associated with other data warehouse solutions.
  • Read this blog to find out how Snowflake uses Automatic Clustering and Materialized Views to improve its internal analytics and the associated benefits.