With interest in big data and cloud increasing around the same time, it wasn’t long until big data began being deployed in the cloud. Big data comes with some challenges when deployed in traditional, on-premises settings. There’s significant operational complexity, and, worst of all, scaling deployments to meet the continued exponential growth of data is difficult, time-consuming, and costly.
In the BigQuery Spotlight series, we talked about Monitoring. This post focuses on using Audit Logs for deep dive monitoring. BigQuery Audit Logs are a collection of logs provided by Google Cloud that provide insight into operations related to your use of BigQuery. A wealth of information is available to you in the Audit Logs. Cloud Logging captures events which can show “who” performed “what” activity and “how” the system behaved.
The right set of tools helps businesses utilize data to drive insights and value. But balancing a strong layer of security and governance with easy access to data for all users is no easy task. Retrofitting existing solutions to ever-changing policy and security demands is one option. Another option — a more rewarding one — is to include centralized data management, security, and governance into data projects from the start.
For decades, the technologies and systems that deliver analytics have undergone massive change. What hasn’t changed, however, is the goal: using data-driven insights to drive actions. Insight to action has been a consistent vision for the industry. Everyone from data practitioners to technology developers have sought this elusive goal, but as Chief Data Strategy Officer Cindi Howson points out, it has remained unfulfilled — until now.
Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing.
The Cloudera Enterprise Data Maturity Report is a global survey of 3,150 business and IT decision makers assessing organizations’ maturity when it comes to their current capabilities and handling of data and analytics.
Stitch is pleased to announce the availability of Microsoft SQL Server as a destination. MS SQL Server joins nine other data destinations (including Microsoft Azure Synapse) that Stitch supports to help execute all your data modeling and analysis projects. Stitch customers can immediately benefit from the new destination, which supports both Azure SQL Server and standard SQL Server editions reaching as far back as SQL Server 2012.
About the report The Cloudera Enterprise Data Maturity Report is a global survey of 3,150 business and IT decision makers assessing organizations’ maturity when it comes to their current capabilities and handling of data and analytics.
The following is Part III of a three-part series. Welcome to the final installment of a three-part series discussing the areas to take seriously when you want to drive business with analytics. In Part I of this series, I discussed how to prioritize data accessibility and how to address the challenges that come with it. Those challenges include: Part II discussed where the disconnect is and addressed how organizations can bridge the gap.
Over the course of my next two blog posts, I would like to share my thoughts around a debate raging in data architecture circles. The bone of contention? That the 21st century needs a new data management paradigm for modern analytics. First up, I’ll frame the argument and explain the two prominent approaches of data hub and data fabric. Then, I’ll cover data mesh and compare all three architectures. As always, I’d love to get your input, feedback, queries and comments!
