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Change Data Capture with Google Analytics
Introduction Let’s pretend you work at a small identity protection startup and you’re getting acquired by a Fortune 100 corporation. You and 120 of your peers are waiting on huge acquisition checks, the champagne is on order, and there’s palpable, anxious excitement in the air. Morale is high and you’re all ready to join the big leagues. There are nervous smiles everywhere you look and you’re waiting to go on a hiring spree to start scaling. You’re feeling good… Until an email comes in from the acquiring corporation’s security and compliance group. You’re being asked some intense questions for
Introduction Imagine that you’re the analytics team lead at an e-commerce company. Your company has seen rapid growth in the last few months, with thousands of new users who are highly engaged on your website. Your leadership team is pushing for the analytics team to dig into user activity data that has been carefully collected inside your production database to figure out how to make your website’s experience even better. Excitedly, one evening you spin up a data warehouse and set up data connectors to replicate data from your database into your data warehouse. The next morning, you wake
Datacoral AWS Startup Architecture 2020
Last year, we had shared that Datacoral had been selected as a finalist for the AWS Startup Architecture competition in the US. Today, we are excited to announce that Datacoral has been declared as the winner of this competition! Many organizations today are struggling to extract and consolidate data from many different data sources into a centralized warehouse. Datacoral provides data connectors for multiple sources (including change data capture connectors for databases) that allow business-critical data to be readily available for analytics for our customers. However, just moving data from source to destination isn’t enough. Data should remain secure,
Secure Data Pipelines in Your Cloud, Fully Managed
Introduction As businesses learn how to make up-to-the-minute, data-driven decisions, their data teams face increasing scope demands. The challenges these data teams face include getting access to a plethora of data sources; understanding the business value that resides in them; managing and moving large data sets; and finding the time to build meaningful dashboards and algorithms that drive business outcomes. Adding more data experts to the team may spread the load, but does not eliminate the myriad distractions from their core role: data analytics. Furthermore, data teams are often working with privileged user data — think patient data in

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