Striim
JULY 31, 2024
At Striim, we’re excited to partner with GigaOm to present an exclusive webinar that promises to shed light on a game-changing topic in the world of data: “The Rise of Streaming Data Platforms: Embrace the Future Now.” Real-time data processing has evolved from a competitive advantage to a necessity.
Cloudera
APRIL 22, 2022
Join Cloudera & Dean Bubley on May 4 for the live webinar Enterprise Data in the 5G Era , to explore how telcos can capitalise on the opportunity of data in the 5G era and help drive the transformation of their enterprise customers. By Dean Bubley, industry analyst and founder of Disruptive Analysis.
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DataKitchen
FEBRUARY 23, 2024
Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability The Need For Personalized Data Journeys for Your Data Consumers Data Teams and Their Types of Data Journeys Webinar: Data Journey: The Missing Piece.
DataKitchen
DECEMBER 7, 2021
The magic sauce of this book lies in the three groups that Jesse outlines and dives into as necessary for data analytics projects to be successful (i.e., the data scientist, the engineer, and the operations engineer). You can purchase Data Teams from its publisher site at Apress here. You can purchase the book here.
Cloudera
DECEMBER 14, 2020
One trend that we’ve seen this year, is that enterprises are leveraging streaming data as a way to traverse through unplanned disruptions, as a way to make the best business decisions for their stakeholders. . Today, a new modern data platform is here to transform how businesses take advantage of real-time analytics.
Knowledge Hut
NOVEMBER 17, 2023
Azure Data Engineers play an important role in building efficient, secure, and intelligent data solutions on Microsoft Azure's powerful platform. The position of Azure Data Engineers is becoming increasingly important as businesses attempt to use the power of data for strategic decision-making and innovation.
Snowflake
AUGUST 22, 2023
In either case, the process of discovery continues to capture the product requirements, including data sources; the specific data requirements such as quality, formats or frequency of refresh; and also analytics and engineering requirements. A prioritization matrix can help formalize this process.
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