2014 Google Cloud Storage 
TechCrunch
FEBRUARY 18, 2022
million developers using the cloud-native tools — an increase of 67% from the year prior. For those who need a refresher on Kubernetes, it is an open source container project created by Google in 2014 to automate, monitor and run applications. In the past year, Kubernetes adoption exploded, with over 5.6
CIO
NOVEMBER 4, 2022
billion company’s scientific, commercial, and manufacturing businesses since joining the company in 2014. And to do this, a move to the cloud was essential. If you are not on the cloud, you are going to be left behind.” Google created some very interesting algorithms and tools that are available in AWS,” McCowan says.
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Lacework
SEPTEMBER 19, 2023
Despite its role out of the spotlight, the process of identifying cloud risk and the corresponding threats to that risk is a critical foundation to any robust security posture. This correlation enables identification of anomalous usage and suspicious activity from cloud identities as soon as they are compromised.
Datavail
DECEMBER 2, 2021
Snowflake took the market by storm when it introduced its innovative cloud-based data warehouse technology in 2014. It is one of the first data warehousing platforms offered as a software as a service (SaaS) product with storage, compute, and cloud services operating separately and billing as independent units.
CircleCI
APRIL 8, 2022
This token enables your CircleCI jobs to authenticate with cloud providers that support OpenID Connect like AWS, Google Cloud Platform, and Vault. OpenID Connect (OIDC) is an authentication protocol that allows cloud services to verify the identity of end users. Introducing OpenID Connect identity tokens in CircleCI jobs!
Altexsoft
APRIL 16, 2021
This could be a transactional database or any other storage we take data from. A transactional or OLTP database is a common storage solution we deal with to record any of our business information. OLAP or Online Analytical Processing aggregates transactional data from a storage to transform it into a feasible form for analysis.
Xebia
JANUARY 19, 2023
The authors divide the data engineer lifecycle into five stages: Generation Storage Ingestion Transformation Serving Data The field is moving up the value chain, incorporating traditional enterprise practices like data management and cost optimization and new practices like DataOps. The central thesis is to stream process all the data.
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